├── AutoSAEA-main ├── AutoSAEA.m ├── DE_optimizer.m ├── DEoperator.m ├── GP_ei_arm.m ├── GP_fit.m ├── GP_lcb_arm.m ├── KNN_eoi_arm.m ├── KNN_eor_arm.m ├── Low_level_r.m ├── PRS_ls_arm.m ├── PRS_pre_arm.m ├── RBF_ls_arm.m ├── RBF_pre_arm.m ├── README.md ├── RUN_AutoSAEA.m ├── TL_UCB.m ├── cec15_test_func.c ├── cec15_test_func.h ├── cec15problems.c ├── cec15problems.mexw64 ├── cec15problems.mexw64.### ├── predictor.m ├── record_result_iml.c └── run_myexperiment.m ├── BDNE └── README.md ├── BSCo-GPLM ├── BSCo-GPLM.zip └── readme.md ├── CEEO_Code ├── Algorithm │ ├── LVDM.py │ ├── My_CEEO.py │ ├── __pycache__ │ │ ├── LVDM.cpython-38.pyc │ │ ├── My_CEEO.cpython-38.pyc │ │ └── categorical_dist.cpython-38.pyc │ └── categorical_dist.py ├── Application │ ├── ParamOP.py │ ├── __pycache__ │ │ ├── ParamOP.cpython-38.pyc │ │ ├── buildAlexNet.cpython-38.pyc │ │ ├── buildLeNet5.cpython-38.pyc │ │ └── evaluation.cpython-38.pyc │ ├── buildAlexNet.py │ ├── buildLeNet5.py │ ├── evaluation.py │ ├── initial_data │ │ ├── AlexNet_cifar10_initial_X.csv │ │ ├── AlexNet_cifar10_initial_data.csv │ │ ├── LeNet5_cifar10_initial_X.csv │ │ └── LeNet5_cifar10_initial_data.csv │ └── train_and_test.py ├── Benchmarks │ ├── DVdata │ │ ├── dvalue_CF1_15_5.txt │ │ ├── dvalue_CF1_5_5.txt │ │ ├── dvalue_CF4_15_5.txt │ │ ├── dvalue_CF4_5_5.txt │ │ ├── dvalue_Lunacek_15_5.txt │ │ ├── dvalue_Lunacek_5_5.txt │ │ ├── dvalue_ackley_15_5.txt │ │ ├── dvalue_ackley_25_5.txt │ │ ├── dvalue_ackley_5_5.txt │ │ ├── dvalue_ellipsoid_15_5.txt │ │ ├── dvalue_ellipsoid_25_5.txt │ │ ├── dvalue_ellipsoid_5_5.txt │ │ ├── dvalue_griewank_15_5.txt │ │ ├── dvalue_griewank_25_5.txt │ │ ├── dvalue_griewank_5_5.txt │ │ ├── dvalue_rastrigin_15_5.txt │ │ ├── dvalue_rastrigin_25_5.txt │ │ ├── dvalue_rastrigin_5_5.txt │ │ ├── dvalue_rosenbrock_15_5.txt │ │ ├── dvalue_rosenbrock_25_5.txt │ │ ├── dvalue_rosenbrock_5_5.txt │ │ ├── dvalue_sphere_15_5.txt │ │ ├── dvalue_sphere_5_5.txt │ │ ├── dvalue_weierstrass_15_5.txt │ │ └── dvalue_weierstrass_5_5.txt │ ├── MVOP.py │ ├── __pycache__ │ │ ├── MVOP.cpython-38.pyc │ │ └── syntheticFunctions.cpython-38.pyc │ ├── shift_data │ │ ├── data_CF1.txt │ │ ├── data_CF4.txt │ │ ├── data_Lunacek.txt │ │ ├── data_ackley.txt │ │ ├── data_ellipsoid.txt │ │ ├── data_elliptic_M_D10.txt │ │ ├── data_griewank.txt │ │ ├── data_rastrigin.txt │ │ ├── data_rosenbrock.txt │ │ ├── data_sphere.txt │ │ └── data_weierstrass.txt │ └── syntheticFunctions.py ├── Cal_pro_ca.py ├── EAs │ ├── DE.py │ ├── De_operator_2.py │ ├── My_Search_Strategy.py │ └── __pycache__ │ │ ├── DE.cpython-38.pyc │ │ ├── De_operator_2.cpython-38.pyc │ │ └── My_Search_Strategy.cpython-38.pyc ├── README.md ├── Run_CEEO_for_Benchmarks_Experiment.py ├── Run_CEEO_for_CNN_Experiment.py ├── Surrogate │ ├── RBFNmv.py │ └── __pycache__ │ │ └── RBFNmv.cpython-38.pyc ├── categorical_set.py ├── datasets │ └── cifar-10-python.tar.gz └── result │ ├── F1.txt │ └── F1_Convergence curve.txt ├── DEORA_code ├── Algorithms │ ├── Algorithm.m │ ├── Multi-task │ │ └── Competitive Multi-task │ │ │ └── DEORA │ │ │ └── DEORA.m │ └── Utils │ │ ├── Cauchy │ │ ├── cauchyinv.m │ │ └── cauchyrnd.m │ │ ├── Constrained │ │ ├── cal_SP.m │ │ ├── min_FP.m │ │ ├── sort_EC.m │ │ ├── sort_FP.m │ │ └── sort_SR.m │ │ ├── DataProcess │ │ └── gen2eva.m │ │ ├── DrawUtils │ │ ├── DrawPopDec.m │ │ └── DrawPopObj.m │ │ ├── Individual │ │ ├── Individual.m │ │ ├── Individual_DE.m │ │ ├── Individual_DE44.m │ │ ├── Individual_MF.m │ │ └── Individual_PSO.m │ │ ├── Initialization.m │ │ ├── Initialization_One.m │ │ ├── Multi-objective │ │ ├── CrowdingDistance.m │ │ ├── NDSort.m │ │ ├── NSGA2Sort.m │ │ ├── Selection_SPEA2.m │ │ └── UniformPoint.m │ │ ├── Multifactorial │ │ ├── Initialization_MF.m │ │ ├── Initialization_MF_MO.m │ │ ├── Initialization_MF_One.m │ │ └── Selection_MF.m │ │ └── Operator │ │ ├── DE_Crossover.m │ │ ├── DE_Crossover_Exp.m │ │ ├── GA_Crossover.m │ │ └── GA_Mutation.m ├── GUI │ ├── MTO_CMD.m │ ├── MTO_GUI.m │ └── Utils │ │ ├── MakeGenEqual.m │ │ ├── Par.m │ │ ├── latexTable.m │ │ └── sort_nat.m ├── Metrics │ ├── CV.m │ ├── FR.m │ ├── HV.m │ ├── HV_CMT.m │ ├── HV_MTS.m │ ├── IGD.m │ ├── IGDp.m │ ├── IGDp_CMT.m │ ├── IGDp_MTS.m │ ├── Obj.m │ ├── Obj_AV.m │ ├── Obj_CMT.m │ ├── Obj_MTS.m │ ├── Obj_NBR.m │ ├── Obj_UV.m │ ├── Run_Time.m │ └── Utils │ │ ├── getHV.m │ │ ├── getIGD.m │ │ └── getIGDp.m ├── Problems │ ├── Base │ │ ├── Ackley.m │ │ ├── Elliptic.m │ │ ├── Griewank.m │ │ ├── Rastrigin.m │ │ ├── Rosenbrock.m │ │ ├── Schwefel.m │ │ ├── Schwefel2.m │ │ ├── Sphere.m │ │ └── Weierstrass.m │ ├── Multi-task │ │ ├── CEC17-MTSO │ │ │ ├── CEC17_MTSO1_CI_HS.m │ │ │ ├── CEC17_MTSO2_CI_MS.m │ │ │ ├── CEC17_MTSO3_CI_LS.m │ │ │ ├── CEC17_MTSO4_PI_HS.m │ │ │ ├── CEC17_MTSO5_PI_MS.m │ │ │ ├── CEC17_MTSO6_PI_LS.m │ │ │ ├── CEC17_MTSO7_NI_HS.m │ │ │ ├── CEC17_MTSO8_NI_MS.m │ │ │ ├── CEC17_MTSO9_NI_LS.m │ │ │ ├── Tasks │ │ │ │ ├── CI_H.mat │ │ │ │ ├── CI_L.mat │ │ │ │ ├── CI_M.mat │ │ │ │ ├── NI_H.mat │ │ │ │ ├── NI_L.mat │ │ │ │ ├── NI_M.mat │ │ │ │ ├── PI_H.mat │ │ │ │ ├── PI_L.mat │ │ │ │ └── PI_M.mat │ │ │ └── benchmark_CEC17_MTSO.m │ │ ├── Competitive-C2TOP │ │ │ ├── Case1 │ │ │ │ ├── C2TOP_Case1_P1.m │ │ │ │ ├── C2TOP_Case1_P2.m │ │ │ │ ├── C2TOP_Case1_P3.m │ │ │ │ ├── C2TOP_Case1_P4.m │ │ │ │ ├── C2TOP_Case1_P5.m │ │ │ │ ├── C2TOP_Case1_P6.m │ │ │ │ ├── C2TOP_Case1_P7.m │ │ │ │ ├── C2TOP_Case1_P8.m │ │ │ │ └── C2TOP_Case1_P9.m │ │ │ ├── Case2 │ │ │ │ ├── C2TOP_Case2_P1.m │ │ │ │ ├── C2TOP_Case2_P2.m │ │ │ │ ├── C2TOP_Case2_P3.m │ │ │ │ ├── C2TOP_Case2_P4.m │ │ │ │ ├── C2TOP_Case2_P5.m │ │ │ │ ├── C2TOP_Case2_P6.m │ │ │ │ └── C2TOP_Case2_P7.m │ │ │ ├── Case3 │ │ │ │ ├── C2TOP_Case3_P1.m │ │ │ │ ├── C2TOP_Case3_P2.m │ │ │ │ ├── C2TOP_Case3_P3.m │ │ │ │ ├── C2TOP_Case3_P4.m │ │ │ │ ├── C2TOP_Case3_P5.m │ │ │ │ ├── C2TOP_Case3_P6.m │ │ │ │ ├── C2TOP_Case3_P7.m │ │ │ │ ├── C2TOP_Case3_P8.m │ │ │ │ └── C2TOP_Case3_P9.m │ │ │ ├── Case4 │ │ │ │ ├── C2TOP_Case4_P1.m │ │ │ │ ├── C2TOP_Case4_P2.m │ │ │ │ ├── C2TOP_Case4_P3.m │ │ │ │ ├── C2TOP_Case4_P4.m │ │ │ │ ├── C2TOP_Case4_P5.m │ │ │ │ ├── C2TOP_Case4_P6.m │ │ │ │ ├── C2TOP_Case4_P7.m │ │ │ │ ├── C2TOP_Case4_P8.m │ │ │ │ └── C2TOP_Case4_P9.m │ │ │ └── benchmark_CEC17_MTSO_Competitive.m │ │ └── Competitive-C4TOP │ │ │ ├── C4TOP1.m │ │ │ ├── C4TOP10.m │ │ │ ├── C4TOP11.m │ │ │ ├── C4TOP12.m │ │ │ ├── C4TOP13.m │ │ │ ├── C4TOP14.m │ │ │ ├── C4TOP15.m │ │ │ ├── C4TOP2.m │ │ │ ├── C4TOP3.m │ │ │ ├── C4TOP4.m │ │ │ ├── C4TOP5.m │ │ │ ├── C4TOP6.m │ │ │ ├── C4TOP7.m │ │ │ ├── C4TOP8.m │ │ │ ├── C4TOP9.m │ │ │ ├── Data │ │ │ ├── M_matrix.mat │ │ │ └── O_matrix.mat │ │ │ └── benchmark_C4TOP.m │ ├── Problem.m │ └── Real-world Applications │ │ └── Sensor Coverage Problem │ │ ├── SCP.m │ │ └── SCP_Adata.mat ├── README.md └── mto.m ├── ECPF&DRS_CAIS ├── ALGORITHM │ ├── MOEAD_Gen │ │ └── MOEAD_Gen.m │ ├── MOEAD_PBI │ │ └── MOEAD_PBI.m │ ├── NSGA-II │ │ ├── EnvironmentalSelection.m │ │ ├── NSGAII.m │ │ └── Operator.m │ ├── SMS-EMOA │ │ ├── CalHV.m │ │ ├── Hypervolume_MEX │ │ │ ├── __MACOSX │ │ │ │ ├── ._release │ │ │ │ └── release │ │ │ │ │ ├── ._.DS_Store │ │ │ │ │ ├── ._Hypervolume.1 │ │ │ │ │ ├── ._Hypervolume_MEX.c │ │ │ │ │ ├── ._Hypervolume_MEX.mexmaci64 │ │ │ │ │ ├── ._Hypervolume_MEX.mexw64 │ │ │ │ │ ├── ._LICENSE │ │ │ │ │ ├── ._Makefile │ │ │ │ │ ├── ._Makefile.lib │ │ │ │ │ ├── ._README │ │ │ │ │ ├── ._R_metric.m │ │ │ │ │ ├── ._cal_metric.m │ │ │ │ │ ├── ._filter_NDS.m │ │ │ │ │ ├── ._getrusage.c │ │ │ │ │ ├── ._hv.c │ │ │ │ │ ├── ._hv.h │ │ │ │ │ ├── ._initweight.m │ │ │ │ │ ├── ._io.c │ │ │ │ │ ├── ._io.h │ │ │ │ │ ├── ._main-hv.c │ │ │ │ │ ├── ._mk │ │ │ │ │ ├── ._multi_layer_weight.m │ │ │ │ │ ├── ._pf_samples.m │ │ │ │ │ ├── ._preprocessing_asf.m │ │ │ │ │ ├── ._resource.h │ │ │ │ │ ├── ._samplingIGD.m │ │ │ │ │ ├── ._svn_version │ │ │ │ │ ├── ._test_data │ │ │ │ │ ├── ._timer.c │ │ │ │ │ ├── ._timer.h │ │ │ │ │ ├── ._trim_cubic.m │ │ │ │ │ ├── mk │ │ │ │ │ ├── ._Darwin_i386_cc.mk │ │ │ │ │ ├── ._Darwin_i386_gcc-4.2.mk │ │ │ │ │ ├── ._Linux_x86_64_icc.mk │ │ │ │ │ ├── ._Linux_x86_64_pathcc.mk │ │ │ │ │ ├── ._Linux_x86_64_pgcc.mk │ │ │ │ │ ├── ._Linux_x86_64_suncc.mk │ │ │ │ │ ├── ._README │ │ │ │ │ ├── ._cc.mk │ │ │ │ │ ├── ._gcc.mk │ │ │ │ │ ├── ._icc.mk │ │ │ │ │ ├── ._pgcc.mk │ │ │ │ │ └── ._suncc.mk │ │ │ │ │ └── test_data │ │ │ │ │ ├── ._.DS_Store │ │ │ │ │ ├── ._RNSGA2 │ │ │ │ │ ├── ._r-stm │ │ │ │ │ ├── RNSGA2 │ │ │ │ │ ├── ._.DS_Store │ │ │ │ │ ├── ._ZDT1M2_1 │ │ │ │ │ └── ZDT1M2_1 │ │ │ │ │ │ ├── ._RNSGA2_FUN0 │ │ │ │ │ │ ├── ._RNSGA2_FUN1 │ │ │ │ │ │ ├── ._RNSGA2_FUN10 │ │ │ │ │ │ ├── ._RNSGA2_FUN11 │ │ │ │ │ │ ├── ._RNSGA2_FUN12 │ │ │ │ │ │ ├── ._RNSGA2_FUN13 │ │ │ │ │ │ ├── ._RNSGA2_FUN14 │ │ │ │ │ │ ├── ._RNSGA2_FUN15 │ │ │ │ │ │ ├── ._RNSGA2_FUN16 │ │ │ │ │ │ ├── ._RNSGA2_FUN17 │ │ │ │ │ │ ├── ._RNSGA2_FUN18 │ │ │ │ │ │ ├── ._RNSGA2_FUN19 │ │ │ │ │ │ ├── ._RNSGA2_FUN2 │ │ │ │ │ │ ├── ._RNSGA2_FUN20 │ │ │ │ │ │ ├── ._RNSGA2_FUN21 │ │ │ │ │ │ ├── ._RNSGA2_FUN22 │ │ │ │ │ │ ├── ._RNSGA2_FUN23 │ │ │ │ │ │ ├── ._RNSGA2_FUN24 │ │ │ │ │ │ ├── ._RNSGA2_FUN25 │ │ │ │ │ │ ├── ._RNSGA2_FUN26 │ │ │ │ │ │ ├── ._RNSGA2_FUN27 │ │ │ │ │ │ ├── ._RNSGA2_FUN28 │ │ │ │ │ │ ├── ._RNSGA2_FUN29 │ │ │ │ │ │ ├── ._RNSGA2_FUN3 │ │ │ │ │ │ ├── ._RNSGA2_FUN30 │ │ │ │ │ │ ├── ._RNSGA2_FUN4 │ │ │ │ │ │ ├── ._RNSGA2_FUN5 │ │ │ │ │ │ ├── ._RNSGA2_FUN6 │ │ │ │ │ │ ├── ._RNSGA2_FUN7 │ │ │ │ │ │ ├── ._RNSGA2_FUN8 │ │ │ │ │ │ └── ._RNSGA2_FUN9 │ │ │ │ │ └── r-stm │ │ │ │ │ ├── ._.DS_Store │ │ │ │ │ ├── ._ZDT1M2_1 │ │ │ │ │ └── ZDT1M2_1 │ │ │ │ │ ├── ._STM_FUN0 │ │ │ │ │ ├── ._STM_FUN1 │ │ │ │ │ ├── ._STM_FUN10 │ │ │ │ │ ├── ._STM_FUN11 │ │ │ │ │ ├── ._STM_FUN12 │ │ │ │ │ ├── ._STM_FUN13 │ │ │ │ │ ├── ._STM_FUN14 │ │ │ │ │ ├── ._STM_FUN15 │ │ │ │ │ ├── ._STM_FUN16 │ │ │ │ │ ├── ._STM_FUN17 │ │ │ │ │ ├── ._STM_FUN18 │ │ │ │ │ ├── ._STM_FUN19 │ │ │ │ │ ├── ._STM_FUN2 │ │ │ │ │ ├── ._STM_FUN20 │ │ │ │ │ ├── ._STM_FUN21 │ │ │ │ │ ├── ._STM_FUN22 │ │ │ │ │ ├── ._STM_FUN23 │ │ │ │ │ ├── ._STM_FUN24 │ │ │ │ │ ├── ._STM_FUN25 │ │ │ │ │ ├── ._STM_FUN26 │ │ │ │ │ ├── ._STM_FUN27 │ │ │ │ │ ├── ._STM_FUN28 │ │ │ │ │ ├── ._STM_FUN29 │ │ │ │ │ ├── ._STM_FUN3 │ │ │ │ │ ├── ._STM_FUN30 │ │ │ │ │ ├── ._STM_FUN4 │ │ │ │ │ ├── ._STM_FUN5 │ │ │ │ │ ├── ._STM_FUN6 │ │ │ │ │ ├── ._STM_FUN7 │ │ │ │ │ ├── ._STM_FUN8 │ │ │ │ │ └── ._STM_FUN9 │ │ │ └── release │ │ │ │ ├── .DS_Store │ │ │ │ ├── ._Hypervolume_MEX.mexmaci64 │ │ │ │ ├── ._Hypervolume_MEX.mexw64 │ │ │ │ ├── Hypervolume.1 │ │ │ │ ├── Hypervolume_MEX.c │ │ │ │ ├── Hypervolume_MEX.mexmaci64 │ │ │ │ ├── Hypervolume_MEX.mexw64 │ │ │ │ ├── LICENSE │ │ │ │ ├── Makefile │ │ │ │ ├── Makefile.lib │ │ │ │ ├── MetricHVandIGD.m │ │ │ │ ├── README │ │ │ │ ├── R_metric.m │ │ │ │ ├── cal_metric.m │ │ │ │ ├── filter_NDS.m │ │ │ │ ├── filter_PF.m │ │ │ │ ├── getrusage.c │ │ │ │ ├── hv.c │ │ │ │ ├── hv.h │ │ │ │ ├── initweight.m │ │ │ │ ├── io.c │ │ │ │ ├── io.h │ │ │ │ ├── main-hv.c │ │ │ │ ├── mk │ │ │ │ ├── Darwin_i386_cc.mk │ │ │ │ ├── Darwin_i386_gcc-4.2.mk │ │ │ │ ├── Linux_x86_64_icc.mk │ │ │ │ ├── Linux_x86_64_pathcc.mk │ │ │ │ ├── Linux_x86_64_pgcc.mk │ │ │ │ ├── Linux_x86_64_suncc.mk │ │ │ │ ├── README │ │ │ │ ├── cc.mk │ │ │ │ ├── gcc.mk │ │ │ │ ├── icc.mk │ │ │ │ ├── pgcc.mk │ │ │ │ └── suncc.mk │ │ │ │ ├── multi_layer_weight.m │ │ │ │ ├── pf_samples.m │ │ │ │ ├── preprocessing_asf.m │ │ │ │ ├── resource.h │ │ │ │ ├── samplingIGD.m │ │ │ │ ├── svn_version │ │ │ │ ├── test_data │ │ │ │ ├── .DS_Store │ │ │ │ ├── RNSGA2 │ │ │ │ │ ├── .DS_Store │ │ │ │ │ └── ZDT1M2_1 │ │ │ │ │ │ ├── RNSGA2_FUN0 │ │ │ │ │ │ ├── RNSGA2_FUN1 │ │ │ │ │ │ ├── RNSGA2_FUN10 │ │ │ │ │ │ ├── RNSGA2_FUN11 │ │ │ │ │ │ ├── RNSGA2_FUN12 │ │ │ │ │ │ ├── RNSGA2_FUN13 │ │ │ │ │ │ ├── RNSGA2_FUN14 │ │ │ │ │ │ ├── RNSGA2_FUN15 │ │ │ │ │ │ ├── RNSGA2_FUN16 │ │ │ │ │ │ ├── RNSGA2_FUN17 │ │ │ │ │ │ ├── RNSGA2_FUN18 │ │ │ │ │ │ ├── RNSGA2_FUN19 │ │ │ │ │ │ ├── RNSGA2_FUN2 │ │ │ │ │ │ ├── RNSGA2_FUN20 │ │ │ │ │ │ ├── RNSGA2_FUN21 │ │ │ │ │ │ ├── RNSGA2_FUN22 │ │ │ │ │ │ ├── RNSGA2_FUN23 │ │ │ │ │ │ ├── RNSGA2_FUN24 │ │ │ │ │ │ ├── RNSGA2_FUN25 │ │ │ │ │ │ ├── RNSGA2_FUN26 │ │ │ │ │ │ ├── RNSGA2_FUN27 │ │ │ │ │ │ ├── RNSGA2_FUN28 │ │ │ │ │ │ ├── RNSGA2_FUN29 │ │ │ │ │ │ ├── RNSGA2_FUN3 │ │ │ │ │ │ ├── RNSGA2_FUN30 │ │ │ │ │ │ ├── RNSGA2_FUN4 │ │ │ │ │ │ ├── RNSGA2_FUN5 │ │ │ │ │ │ ├── RNSGA2_FUN6 │ │ │ │ │ │ ├── RNSGA2_FUN7 │ │ │ │ │ │ ├── RNSGA2_FUN8 │ │ │ │ │ │ └── RNSGA2_FUN9 │ │ │ │ └── r-stm │ │ │ │ │ ├── .DS_Store │ │ │ │ │ └── ZDT1M2_1 │ │ │ │ │ ├── STM_FUN0 │ │ │ │ │ ├── STM_FUN1 │ │ │ │ │ ├── STM_FUN10 │ │ │ │ │ ├── STM_FUN11 │ │ │ │ │ ├── STM_FUN12 │ │ │ │ │ ├── STM_FUN13 │ │ │ │ │ ├── STM_FUN14 │ │ │ │ │ ├── STM_FUN15 │ │ │ │ │ ├── STM_FUN16 │ │ │ │ │ ├── STM_FUN17 │ │ │ │ │ ├── STM_FUN18 │ │ │ │ │ ├── STM_FUN19 │ │ │ │ │ ├── STM_FUN2 │ │ │ │ │ ├── STM_FUN20 │ │ │ │ │ ├── STM_FUN21 │ │ │ │ │ ├── STM_FUN22 │ │ │ │ │ ├── STM_FUN23 │ │ │ │ │ ├── STM_FUN24 │ │ │ │ │ ├── STM_FUN25 │ │ │ │ │ ├── STM_FUN26 │ │ │ │ │ ├── STM_FUN27 │ │ │ │ │ ├── STM_FUN28 │ │ │ │ │ ├── STM_FUN29 │ │ │ │ │ ├── STM_FUN3 │ │ │ │ │ ├── STM_FUN30 │ │ │ │ │ ├── STM_FUN4 │ │ │ │ │ ├── STM_FUN5 │ │ │ │ │ ├── STM_FUN6 │ │ │ │ │ ├── STM_FUN7 │ │ │ │ │ ├── STM_FUN8 │ │ │ │ │ └── STM_FUN9 │ │ │ │ ├── timer.c │ │ │ │ ├── timer.h │ │ │ │ └── trim_cubic.m │ │ ├── Reduce.m │ │ ├── SMSEMOA.m │ │ └── UpdateFront.m │ ├── e-MOEA │ │ ├── UpdateArchive.m │ │ ├── UpdatePopulation.m │ │ └── eMOEA.m │ └── mNSGA-II │ │ ├── CrowdingDistance.m │ │ ├── EnvironmentalSelection_modified.m │ │ └── mNSGAII.m ├── README.txt └── TESTPROBLEM │ ├── MOP-CH │ └── MOP_CH.m │ └── mDTLZ │ ├── mDTLZ1.m │ ├── mDTLZ2.m │ ├── mDTLZ3.m │ └── mDTLZ4.m ├── IDRCEA-main ├── DEbest.m ├── DErand.m ├── EDA.m ├── IDRCEA.m ├── README.md ├── RUN_IDRCEA.m ├── my_rbfbuild.m ├── my_rbfpredict.m ├── run_myexperiment.m ├── srgtsFitCreateState.m ├── srgtsRBFEvaluate.m ├── srgtsRBFFit.m └── srgtsRBFSetOptions.m ├── IMOEA-ARP ├── IMOEA-ARP │ ├── CalHV.m │ ├── Hypervolume_MEX.rar │ ├── IMOEA_ARP.m │ ├── Reduce.m │ ├── Reduce_adaptive.m │ └── UpdateFront.m ├── README.md ├── Rectangle Problem │ └── Rectangle_Problem.m └── SHDB-MOP │ ├── SHDB_MOP1.m │ ├── SHDB_MOP2.m │ ├── SHDB_MOP3.m │ ├── SHDB_MOP4.m │ ├── SHDB_MOP5.m │ ├── SHDB_MOP6.m │ ├── SHDB_MOP7.m │ ├── SHDB_MOP8.m │ └── SHDB_MOP9.m ├── MOEA-D-ACN └── README.md ├── MOEA-ESD ├── MOEA-ESD │ ├── EnvironmentalSelection.m │ └── MOEA_ESD.m ├── README.md ├── mDTLZ │ ├── emDTLZ1.m │ ├── emDTLZ2.m │ ├── emDTLZ3.m │ ├── emDTLZ4.m │ ├── mDTLZ1.m │ ├── mDTLZ2.m │ ├── mDTLZ3.m │ └── mDTLZ4.m └── mUF │ ├── emUF1.m │ ├── emUF10.m │ ├── emUF2.m │ ├── emUF3.m │ ├── emUF4.m │ ├── emUF7.m │ ├── emUF8.m │ ├── mUF1.m │ ├── mUF10.m │ ├── mUF2.m │ ├── mUF3.m │ ├── mUF4.m │ ├── mUF7.m │ └── mUF8.m ├── MOEAD-ASS_Code ├── MOEAD-ASS.zip └── README.md ├── MOEAD-RD ├── MOEAD_MD │ ├── __init__.py │ ├── evaluate.py │ ├── moead_base.py │ ├── moead_md.py │ ├── my_io.py │ ├── packing.py │ ├── process_data.py │ └── routing.py ├── README.md ├── data │ ├── F1F2s.json │ └── inputs250 │ │ ├── E1594518281316 │ │ ├── E1594603676353 │ │ ├── E1594608783313 │ │ ├── E1594609507040 │ │ ├── E1594609704445 │ │ ├── E1594609968101 │ │ ├── E1594610203092 │ │ ├── E1594610289400 │ │ ├── E1594621057399 │ │ ├── E1594624572014 │ │ ├── E1594624591350 │ │ ├── E1594627576616 │ │ ├── E1594627722757 │ │ ├── E1594630578881 │ │ ├── E1594631052968 │ │ ├── E1594686208445 │ │ ├── E1594686645151 │ │ ├── E1594689653535 │ │ ├── E1594862078693 │ │ ├── E1594863032241 │ │ ├── E1594871546666 │ │ ├── E1594946979367 │ │ ├── E1595294354093 │ │ ├── E1595297459447 │ │ ├── E1595298189657 │ │ ├── E1595312074898 │ │ ├── E1595312557577 │ │ ├── E1595380748607 │ │ ├── E1595552642835 │ │ ├── E1595564649440 │ │ ├── E1595638253198 │ │ ├── E1595638696418 │ │ ├── E1595640283427 │ │ ├── E1595640678717 │ │ ├── E1595666762206 │ │ ├── E1595667031467 │ │ ├── E1595814653661 │ │ ├── E1595814777127 │ │ ├── E1595819027950 │ │ ├── E1595822118159 │ │ ├── E1595840306724 │ │ ├── E1595897832070 │ │ ├── E1595901022575 │ │ ├── E1595901412586 │ │ ├── E1595984926476 │ │ ├── E1595986229376 │ │ ├── E1595994586443 │ │ ├── E1596184497155 │ │ ├── E1596332905752 │ │ ├── E1596419978938 │ │ ├── E1596420272061 │ │ ├── E1596422326759 │ │ ├── E1596423598552 │ │ ├── E1596447226540 │ │ ├── E1596503530161 │ │ ├── E1596504946456 │ │ ├── E1596504980633 │ │ ├── E1596505279153 │ │ ├── E1596505391785 │ │ ├── E1596505632436 │ │ ├── E1596506165339 │ │ ├── E1596589199975 │ │ ├── E1596589836664 │ │ ├── E1596590020798 │ │ ├── E1596590296306 │ │ ├── E1596591095311 │ │ ├── E1596591311707 │ │ ├── E1596592714664 │ │ ├── E1596593449100 │ │ ├── E1596597917959 │ │ ├── E1596620493845 │ │ ├── E1596620827989 │ │ ├── E1596674320452 │ │ ├── E1596675522151 │ │ ├── E1596676780873 │ │ ├── E1596680011218 │ │ ├── E1596762404224 │ │ ├── E1596762540242 │ │ ├── E1596763319388 │ │ ├── E1596943238153 │ │ ├── E1596943422130 │ │ ├── E1597021927994 │ │ ├── E1597022041700 │ │ ├── E1597022090932 │ │ ├── E1597047714526 │ │ ├── E1597106705198 │ │ ├── E1597106833368 │ │ ├── E1597107861320 │ │ ├── E1597108021277 │ │ ├── E1597108254261 │ │ ├── E1597109040483 │ │ ├── E1597109755253 │ │ ├── E1597110057826 │ │ ├── E1597111671701 │ │ ├── E1597112047246 │ │ ├── E1597113981390 │ │ ├── E1597117485423 │ │ ├── E1597117599578 │ │ ├── E1597148339864 │ │ ├── E1597195009322 │ │ ├── E1597195301689 │ │ ├── E1597200028766 │ │ ├── E1597200332928 │ │ ├── E1597200495349 │ │ ├── E1597200518494 │ │ ├── E1597202461634 │ │ ├── E1597213930116 │ │ ├── E1597217704652 │ │ ├── E1597235153425 │ │ ├── E1597281281876 │ │ ├── E1597282936387 │ │ ├── E1597283167355 │ │ ├── E1597284418604 │ │ ├── E1597284477219 │ │ ├── E1597286716540 │ │ ├── E1597286728603 │ │ ├── E1597286860235 │ │ ├── E1597288689701 │ │ ├── E1597288890554 │ │ ├── E1597302276309 │ │ ├── E1597304598689 │ │ ├── E1597304611829 │ │ ├── E1597305374056 │ │ ├── E1597305498461 │ │ ├── E1597310204780 │ │ ├── E1597320420466 │ │ ├── E1597366805314 │ │ ├── E1597372945362 │ │ ├── E1597373193320 │ │ ├── E1597373872717 │ │ ├── E1597376969336 │ │ ├── E1597540749327 │ │ ├── E1597542875641 │ │ ├── E1597543471203 │ │ ├── E1597544118673 │ │ ├── E1597544568924 │ │ ├── E1597544680576 │ │ ├── E1597624862633 │ │ ├── E1597630522943 │ │ ├── E1597631391043 │ │ ├── E1597633576212 │ │ ├── E1597655794555 │ │ ├── E1597712499463 │ │ ├── E1597713417662 │ │ ├── E1597713733580 │ │ ├── E1597714219599 │ │ ├── E1597714753825 │ │ ├── E1597716465849 │ │ ├── E1597717167103 │ │ ├── E1597717895035 │ │ ├── E1597722352821 │ │ ├── E1597732804328 │ │ ├── E1597796806162 │ │ ├── E1597797942369 │ │ ├── E1597802247495 │ │ ├── E1597802825734 │ │ ├── E1597803640177 │ │ ├── E1597804117692 │ │ ├── E1597807290558 │ │ ├── E1597808822038 │ │ ├── E1597809360188 │ │ ├── E1597809658427 │ │ ├── E1597809780083 │ │ ├── E1597809878442 │ │ ├── E1597810596684 │ │ ├── E1597820112961 │ │ ├── E1597884759924 │ │ ├── E1597884767607 │ │ ├── E1597888881042 │ │ ├── E1597889447447 │ │ ├── E1597971291364 │ │ ├── E1597972114950 │ │ ├── E1597979660258 │ │ ├── E1597981906696 │ │ ├── E1598058457340 │ │ ├── E1598231860985 │ │ ├── E1598234802491 │ │ ├── E1598316126286 │ │ ├── E1598316606736 │ │ ├── E1598316643970 │ │ ├── E1598316937582 │ │ ├── E1598317054855 │ │ ├── E1598317160241 │ │ ├── E1598317537946 │ │ ├── E1598317750414 │ │ ├── E1598320041143 │ │ ├── E1598324627286 │ │ ├── E1598325300472 │ │ ├── E1598325459084 │ │ ├── E1598325766890 │ │ ├── E1598325832001 │ │ ├── E1598402314914 │ │ ├── E1598402661803 │ │ ├── E1598403421323 │ │ ├── E1598410634167 │ │ ├── E1598410672329 │ │ ├── E1598414359366 │ │ ├── E1598424346335 │ │ ├── E1598424517049 │ │ ├── E1598424619770 │ │ ├── E1598428048275 │ │ ├── E1598428999777 │ │ ├── ECO2007120014 │ │ ├── ECO2007120017 │ │ ├── ECO2007120020 │ │ ├── ECO2007120021 │ │ ├── ECO2007120026 │ │ ├── ECO2007130027 │ │ ├── ECO2007130086 │ │ ├── ECO2007130100 │ │ ├── ECO2007210113 │ │ ├── ECO2007240027 │ │ ├── ECO2007250011 │ │ ├── ECO2007270107 │ │ ├── ECO2007290014 │ │ ├── ECO2007300009 │ │ ├── ECO2008070012 │ │ ├── ECO2008110020 │ │ ├── ECO2008110027 │ │ ├── ECO2008120023 │ │ ├── ECO2008120038 │ │ ├── ECO2008130066 │ │ ├── ECO2008130135 │ │ ├── ECO2008140098 │ │ ├── ECO2008160002 │ │ ├── ECO2008190025 │ │ ├── ECO2008220028 │ │ ├── ECO2008250134 │ │ ├── ECO2008250157 │ │ ├── ECO2008260084 │ │ ├── ECO2008260104 │ │ ├── ECO2008270042 │ │ ├── EF1594616089763 │ │ ├── EF1594632252863 │ │ ├── EF1594805700879 │ │ ├── EF1594955642012 │ │ ├── EF1595231952604 │ │ ├── EF1595295309474 │ │ ├── EF1595295895624 │ │ ├── EF1595381687057 │ │ ├── EF1595474862428 │ │ ├── EF1595650233198 │ │ ├── EF1595817642706 │ │ ├── EF1595836068288 │ │ ├── EF1596011846243 │ │ ├── EF1596102693660 │ │ ├── EF1596466497583 │ │ ├── EF1596525590000 │ │ ├── EF1596590704042 │ │ └── EF1597377226190 ├── getHV.py ├── global_var.py ├── run.py └── utils │ ├── gene_sols_utils.py │ ├── getHV_utils.py │ ├── moead_utils.py │ └── utils.py ├── MTOTC ├── MTOTC_codes.rar └── readme.md ├── On-GMMOR ├── EnvironmentalSelection_OR.m ├── GMMOR.m ├── GMM_cluster.m ├── LHA.m ├── NLHA.asv ├── On_GMMOR.asv ├── On_GMMOR.m └── README.md ├── README.md └── SAMFEO_Code ├── Generator_DE.m ├── RBFCreate.m ├── RBFInterp.m ├── Repair.m ├── SAMFEO.m ├── cec17_func.cpp ├── cec17_func.mexw32 ├── cec17_func.mexw64 ├── input_data.zip └── randFCR.m /AutoSAEA-main/DEoperator.m: -------------------------------------------------------------------------------- 1 | % DEoperating consist of mutation and crossover 2 | function [U] = DEoperating(P,NP,Dim,hisx,F,CR,UB,LB) 3 | for i=1:NP 4 | % mutation 5 | k0=randi([1,NP]); 6 | while(k0==i) 7 | k0=randi([1,NP]); 8 | end 9 | P1=P(k0,:); 10 | k1=randi([1,NP]); 11 | while(k1==i||k1==k0) 12 | k1=randi([1,NP]); 13 | end 14 | P2=P(k1,:); 15 | k2=randi([1,NP]); 16 | while(k2==i||k2==k1||k2==k0) 17 | k2=randi([1,NP]); 18 | end 19 | P3=P(k2,:); 20 | 21 | Xpbest = hisx(1,:); 22 | V(i,:)=P(i,:)+F.*(Xpbest-P(i,:))+F.*(P2-P3); 23 | 24 | % bound 25 | for j=1:Dim 26 | if (V(i,j)>UB(i,j)||V(i,j)= lvalue: 11 | idx2 = i 12 | break 13 | return idx2 14 | 15 | 16 | 17 | def Roulette(len_c, v_dv, pro_matrix): 18 | 19 | x_c = np.zeros([1, len_c]) 20 | for j in range(0, len_c): 21 | jtemp = np.argwhere(np.random.random() <= pro_matrix[j, :]) 22 | 23 | Select_ca = jtemp[0] 24 | 25 | 26 | x_c[0, j] = v_dv[j, Select_ca] 27 | x_c = x_c.astype(float) 28 | return x_c 29 | 30 | 31 | ################################# 32 | def DEUCB(K , M , database, len_r, len_c, dn_r, up_r, N_lst, v_dv, pro_matrix): 33 | 34 | pop_x = database[0][:K] 35 | 36 | 37 | x_c_generate = np.zeros((M,len_c)) 38 | 39 | 40 | x_r_generate = DE_2(pop_x[:, :len_r], 0.5, 0.8, up_r, dn_r) 41 | for i in range(0, M): 42 | 43 | x_c_generate[i, :] = Roulette(len_c, v_dv, pro_matrix) 44 | 45 | return x_r_generate, x_c_generate 46 | ################################# -------------------------------------------------------------------------------- /CEEO_Code/EAs/__pycache__/DE.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/CEEO_Code/EAs/__pycache__/DE.cpython-38.pyc -------------------------------------------------------------------------------- /CEEO_Code/EAs/__pycache__/De_operator_2.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/CEEO_Code/EAs/__pycache__/De_operator_2.cpython-38.pyc -------------------------------------------------------------------------------- /CEEO_Code/EAs/__pycache__/My_Search_Strategy.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/CEEO_Code/EAs/__pycache__/My_Search_Strategy.cpython-38.pyc -------------------------------------------------------------------------------- /CEEO_Code/README.md: -------------------------------------------------------------------------------- 1 | # CEEO 2 | Customized Evolutionary Expensive Optimization: Efficient Search and Surrogate Strategies for Continuous and Categorical Variables 3 | 4 | ------------------------------- Reference -------------------------------- 5 | 6 | Z.W, L. X.,et al. Customized Evolutionary Expensive Optimization: Efficient Search and Surrogate Strategies for Continuous and Categorical Variables in IEEE Transactions on Systems Man Cybernetics-Systems. 7 | 8 | ------------------------------- Copyright -------------------------------- 9 | 10 | Permission is granted to copy and use this code for research, noncommercial purposes, provided this copyright notice is retained and the origin of the code is cited. The code is provided "as is" and without any warranties, express or implied. 11 | 12 | If you have further questions, please contact me. My email address is: lindong.xie@connect.polyu.hk 13 | 14 | 15 | If you would like to run CEEO on benchmark experiments, please execute Run_CEEO_for_Benchmarks_Experiment.py. To apply CEEO for CNN parameter optimization, please execute Run_CEEO_for_CNN_Experiment.py. 16 | 17 | 18 | ## run benchmarks requirements: 19 | + python == 3.8 20 | + smt == 1.1.0 21 | + numpy == 1.24.4 22 | + scipy == 1.8.1 23 | 24 | ## run application requirements: 25 | + pandas == 1.5.0 26 | + torch == 2.1.0 27 | + torchvision == 0.16.0 28 | -------------------------------------------------------------------------------- /CEEO_Code/Surrogate/__pycache__/RBFNmv.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/CEEO_Code/Surrogate/__pycache__/RBFNmv.cpython-38.pyc -------------------------------------------------------------------------------- /CEEO_Code/datasets/cifar-10-python.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/CEEO_Code/datasets/cifar-10-python.tar.gz -------------------------------------------------------------------------------- /CEEO_Code/result/F1.txt: -------------------------------------------------------------------------------- 1 | 5.969983957879287203e-27 2 | 1.133378034072533839e-26 3 | 1.193868878049705859e-27 4 | 5.517204701900482178e+03 5 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Cauchy/cauchyrnd.m: -------------------------------------------------------------------------------- 1 | function r = cauchyrnd(varargin) 2 | % USAGE: r= cauchyrnd(a, b, n, ...) 3 | % 4 | % Generate random numbers from the Cauchy distribution, r= a + b*tan(pi*(rand(n)-0.5)). 5 | % 6 | % ARGUMENTS: 7 | % a (default value: 0.0) must be scalars or size(x). 8 | % b (b>0, default value: 1.0) must be scalars or size(x). 9 | % n and onwards (default value: 1) specifies the dimension of the output. 10 | % 11 | % EXAMPLE: 12 | % r= cauchyrnd(0, 1, 10); % A 10 by 10 array of random values, Cauchy distributed. 13 | % 14 | % SEE ALSO: cauchycdf, cauchyfit, cauchyinv, cauchypdf. 15 | % 16 | % Copyright (C) Peder Axensten 17 | % 18 | % HISTORY: 19 | % Version 1.0, 2006-07-10. 20 | % Version 1.1, 2006-07-26. 21 | % - Added cauchyfit to the cauchy package. 22 | % Version 1.2, 2006-07-31: 23 | % - cauchyinv(0, ...) returned a large negative number but should be -Inf. 24 | % - Size comparison in argument check didn't work. 25 | % - Various other improvements to check list. 26 | 27 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 28 | 29 | % Default values 30 | a = 0.0; 31 | b = 1.0; 32 | n = 1; 33 | 34 | % Check the arguments 35 | if (nargin >= 1) 36 | a = varargin{1}; 37 | if (nargin >= 2) 38 | b = varargin{2}; 39 | b(b <= 0) = NaN; % Make NaN of out of range values. 40 | if (nargin >= 3), n = [varargin{3:end}]; end 41 | end 42 | end 43 | 44 | % Generate 45 | r = cauchyinv(rand(n), a, b); 46 | end 47 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Constrained/cal_SP.m: -------------------------------------------------------------------------------- 1 | function f = cal_SP(Obj, CV, varargin) 2 | %% Self-adaptive Penalty 3 | 4 | n = numel(varargin); 5 | if n == 0 6 | type = 1; 7 | elseif n == 1 8 | type = varargin{1}; 9 | end 10 | 11 | if type == 1 12 | fsort = sort(Obj); 13 | if fsort(end) == fsort(1) 14 | fnorm = ones(1, length(Obj)); 15 | else 16 | fnorm = (Obj - fsort(1)) ./ (fsort(end) - fsort(1)); 17 | end 18 | cv_max = max(CV); 19 | if ~(cv_max == 0) 20 | CV = CV / cv_max; 21 | end 22 | f_idx = find(CV == 0); 23 | rf = length(f_idx) / length(Obj); 24 | if rf == 0 25 | X = zeros(1, length(Obj)); 26 | d = CV; 27 | else 28 | X = CV; 29 | d = sqrt(fnorm.^2 + CV.^2); 30 | end 31 | Y = fnorm; 32 | Y(f_idx) = zeros(1, length(f_idx)); 33 | p = (1 - rf) .* X + (rf .* Y); 34 | f = d + p; 35 | elseif type == 2 36 | bestfeas = min(Obj); 37 | cv_max = max(CV); 38 | if cv_max == 0 39 | f = Obj; 40 | else 41 | f = bestfeas + abs(Obj - bestfeas) .* abs(CV ./ cv_max); 42 | end 43 | end 44 | end 45 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Constrained/min_FP.m: -------------------------------------------------------------------------------- 1 | function [minObj, minCV, min_idx] = min_FP(Obj, CV, varargin) 2 | %% Minimal Feasible Priority / Epsilon Constraint 3 | 4 | n = numel(varargin); 5 | if n == 0 6 | ep = 0; 7 | elseif n == 1 8 | ep = varargin{1}; 9 | end 10 | CV(CV <= ep) = 0; 11 | 12 | minCV = min(CV); 13 | idx_min_cv = find(CV == minCV); 14 | obj_temp = Obj(idx_min_cv); 15 | [minObj, idx_temp] = min(obj_temp); 16 | min_idx = idx_min_cv(idx_temp); 17 | end 18 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Constrained/sort_EC.m: -------------------------------------------------------------------------------- 1 | function rank = sort_EC(Obj, CV, ep) 2 | %% Epsilon Constraint Sort 3 | Obj = reshape(Obj, length(Obj), 1); 4 | CV = reshape(CV, length(CV), 1); 5 | CV(CV <= ep) = 0; 6 | [~, rank] = sortrows([CV, Obj], [1, 2]); 7 | rank = rank'; 8 | end 9 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Constrained/sort_FP.m: -------------------------------------------------------------------------------- 1 | function rank = sort_FP(Obj, CV, varargin) 2 | %% Feasible Priority Sort 3 | Obj = reshape(Obj, length(Obj), 1); 4 | CV = reshape(CV, length(CV), 1); 5 | [~, rank] = sortrows([CV, Obj], [1, 2]); 6 | rank = rank'; 7 | end 8 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Constrained/sort_SR.m: -------------------------------------------------------------------------------- 1 | function idx = sort_SR(Obj, CV, sr) 2 | %% Stochastic Ranking Sort 3 | idx = 1:length(Obj); 4 | for i = 1:length(Obj) 5 | swap = 0; 6 | for j = 1:length(Obj) - i 7 | if (0 == CV(j) && 0 == CV(j + 1)) || rand() < sr 8 | if Obj(j) > Obj(j + 1) 9 | t = idx(j); t_obj = Obj(j); t_cv = CV(j); 10 | idx(j) = idx(j + 1); Obj(j) = Obj(j + 1); CV(j) = CV(j + 1); 11 | idx(j + 1) = t; Obj(j + 1) = t_obj; CV(j + 1) = t_cv; 12 | swap = 1; 13 | end 14 | else 15 | if CV(j) > CV(j + 1) 16 | t = idx(j); t_obj = Obj(j); t_cv = CV(j); 17 | idx(j) = idx(j + 1); Obj(j) = Obj(j + 1); CV(j) = CV(j + 1); 18 | idx(j + 1) = t; Obj(j + 1) = t_obj; CV(j + 1) = t_cv; 19 | swap = 1; 20 | end 21 | end 22 | end 23 | if swap == 0 24 | break; 25 | end 26 | end 27 | end 28 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/DataProcess/gen2eva.m: -------------------------------------------------------------------------------- 1 | function Result = gen2eva(Result_Gen, FE_Gen, maxGen) 2 | %% Map the convergence from generation to evaluation 3 | % Input: Result_Gen, FE_Gen, maxGen 4 | % Output: Result 5 | 6 | %------------------------------- Copyright -------------------------------- 7 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 8 | % purposes. All publications which use this platform should acknowledge 9 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 10 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 11 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 12 | %-------------------------------------------------------------------------- 13 | 14 | if size(Result_Gen, 2) <= maxGen 15 | maxGen = size(Result_Gen, 2); 16 | end 17 | 18 | Result = Result_Gen(:, 1:maxGen); 19 | for k = 1:size(Result_Gen, 1) 20 | Gap = FE_Gen(end) ./ (maxGen); 21 | idx = 1; 22 | i = 1; 23 | while i <= length(FE_Gen) 24 | if FE_Gen(i) >= ((idx) * Gap) 25 | Result(k, idx) = Result_Gen(k, i); 26 | idx = idx + 1; 27 | else 28 | i = i + 1; 29 | end 30 | if idx > maxGen 31 | break; 32 | end 33 | end 34 | Result(k, 1) = Result_Gen(k, 1); 35 | Result(k, end) = Result_Gen(k, end); 36 | if idx < maxGen 37 | for x = idx:maxGen 38 | Result(k, x) = Result_Gen(k, end); 39 | end 40 | end 41 | end 42 | end 43 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Individual/Individual.m: -------------------------------------------------------------------------------- 1 | classdef Individual 2 | 3 | %------------------------------- Copyright -------------------------------- 4 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 5 | % purposes. All publications which use this platform should acknowledge 6 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 7 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 8 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 9 | %-------------------------------------------------------------------------- 10 | 11 | %% Individual Base Class 12 | 13 | properties 14 | Dec % decision variables 15 | Obj % objective value 16 | CV % constraint violation 17 | end 18 | 19 | methods 20 | function value = Decs(obj) 21 | value = cat(1, obj.Dec); 22 | end 23 | 24 | function value = Objs(obj) 25 | value = cat(1, obj.Obj); 26 | end 27 | 28 | function value = CVs(obj) 29 | value = cat(1, obj.CV); 30 | end 31 | end 32 | end 33 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Individual/Individual_DE.m: -------------------------------------------------------------------------------- 1 | classdef Individual_DE < Individual 2 | 3 | %------------------------------- Copyright -------------------------------- 4 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 5 | % purposes. All publications which use this platform should acknowledge 6 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 7 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 8 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 9 | %-------------------------------------------------------------------------- 10 | 11 | properties 12 | F 13 | CR 14 | end 15 | end 16 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Individual/Individual_DE44.m: -------------------------------------------------------------------------------- 1 | classdef Individual_DE44 < Individual_DE 2 | 3 | %------------------------------- Copyright -------------------------------- 4 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 5 | % purposes. All publications which use this platform should acknowledge 6 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 7 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 8 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 9 | %-------------------------------------------------------------------------- 10 | 11 | properties 12 | ST % Strategy 13 | end 14 | end 15 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Individual/Individual_MF.m: -------------------------------------------------------------------------------- 1 | classdef Individual_MF < Individual 2 | 3 | %------------------------------- Copyright -------------------------------- 4 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 5 | % purposes. All publications which use this platform should acknowledge 6 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 7 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 8 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 9 | %-------------------------------------------------------------------------- 10 | 11 | properties 12 | % multifactorial parameter 13 | MFObj % multifactorial Objective 14 | MFCV % multifactorial Constraint Violation 15 | MFRank % multifactorial rank 16 | MFFactor % multifactorial Skill Factor 17 | end 18 | end 19 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Individual/Individual_PSO.m: -------------------------------------------------------------------------------- 1 | classdef Individual_PSO < Individual 2 | %% Individual for PSO 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 6 | % purposes. All publications which use this platform should acknowledge 7 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 8 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 9 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 10 | %-------------------------------------------------------------------------- 11 | 12 | properties 13 | PBestDec 14 | PBestObj 15 | PBestCV 16 | V = 0 17 | end 18 | end 19 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Initialization.m: -------------------------------------------------------------------------------- 1 | function population = Initialization(Algo, Prob, Individual_Class, varargin) 2 | %% Multi-task - Initialize and evaluate the population 3 | % Input: Algorithm, Problem, Individual_Class 4 | % Output: population 5 | 6 | %------------------------------- Copyright -------------------------------- 7 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 8 | % purposes. All publications which use this platform should acknowledge 9 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 10 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 11 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 12 | %-------------------------------------------------------------------------- 13 | 14 | n = numel(varargin); 15 | if n == 0 16 | N = Prob.N; 17 | elseif n == 1 18 | N = varargin{1}; 19 | else 20 | return; 21 | end 22 | 23 | for t = 1:Prob.T 24 | for i = 1:N 25 | population{t}(i) = Individual_Class(); 26 | % switch gene_type 27 | % case 'unified' 28 | population{t}(i).Dec = rand(1, max(Prob.D)); 29 | % case 'real' 30 | % population{t}(i).Dec = (Prob.Ub{t} - Prob.Lb{t}) .* rand(1, max(Prob.D)) + Prob.Lb{t}; 31 | % end 32 | end 33 | population{t} = Algo.Evaluation(population{t}, Prob, t); 34 | end 35 | end 36 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Initialization_One.m: -------------------------------------------------------------------------------- 1 | function population = Initialization_One(Algo, Prob, t, Individual_Class, varargin) 2 | %% Initialize and evaluate the population for One task 3 | % Input: Algorithm, Problem, task_idx, Individual_Class 4 | % Output: population 5 | 6 | %------------------------------- Copyright -------------------------------- 7 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 8 | % purposes. All publications which use this platform should acknowledge 9 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 10 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 11 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 12 | %-------------------------------------------------------------------------- 13 | 14 | n = numel(varargin); 15 | if n == 0 16 | N = Prob.N; 17 | elseif n == 1 18 | N = varargin{1}; 19 | else 20 | return; 21 | end 22 | 23 | for i = 1:N 24 | population(i) = Individual_Class(); 25 | % switch gene_type 26 | % case 'unified' 27 | population(i).Dec = rand(1, max(Prob.D)); 28 | % case 'real' 29 | % population(i).Dec = (Prob.Ub{t} - Prob.Lb{t}) .* rand(1, max(Prob.D)) + Prob.Lb{t}; 30 | % end 31 | end 32 | population = Algo.Evaluation(population, Prob, t); 33 | end 34 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Multi-objective/NSGA2Sort.m: -------------------------------------------------------------------------------- 1 | function [rank, FrontNo, CrowdDis] = NSGA2Sort(population) 2 | FrontNo = NDSort(population.Objs, population.CVs, inf); 3 | CrowdDis = CrowdingDistance(population.Objs, FrontNo); 4 | [~, rank] = sortrows([FrontNo', -CrowdDis']); 5 | end 6 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Multifactorial/Initialization_MF_MO.m: -------------------------------------------------------------------------------- 1 | function population = Initialization_MF_MO(Algo, Prob, Individual_Class) 2 | %% Multifactorial-Multi-objective - Initialize and evaluate the population 3 | % Input: Algorithm, Problem, Individual_Class 4 | % Output: population 5 | 6 | %------------------------------- Copyright -------------------------------- 7 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 8 | % purposes. All publications which use this platform should acknowledge 9 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 10 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 11 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 12 | %-------------------------------------------------------------------------- 13 | 14 | % Generate initial population 15 | population = Individual_Class.empty(); 16 | for t = 1:Prob.T 17 | for i = 1:Prob.N 18 | pop_t(i) = Individual_Class(); 19 | pop_t(i).Dec = rand(1, max(Prob.D)); 20 | end 21 | pop_t = Algo.Evaluation(pop_t, Prob, t); 22 | for i = 1:length(pop_t) 23 | pop_t(i).MFFactor = t; 24 | end 25 | population = [population, pop_t]; 26 | end 27 | end 28 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Multifactorial/Initialization_MF_One.m: -------------------------------------------------------------------------------- 1 | function population = Initialization_MF_One(Algo, Prob, Individual_Class) 2 | %% Multifactorial - Initialize and evaluate the population One Times 3 | % Input: Algorithm, Problem, Individual_Class 4 | % Output: population 5 | 6 | %------------------------------- Copyright -------------------------------- 7 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 8 | % purposes. All publications which use this platform should acknowledge 9 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 10 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 11 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 12 | %-------------------------------------------------------------------------- 13 | 14 | % Generate initial population 15 | population = Individual_Class.empty(); 16 | for t = 1:Prob.T 17 | for i = 1:Prob.N 18 | pop_t(i) = Individual_Class(); 19 | pop_t(i).Dec = rand(1, max(Prob.D)); 20 | end 21 | pop_t = Algo.Evaluation(pop_t, Prob, t); 22 | for i = 1:length(pop_t) 23 | pop_t(i).MFFactor = t; 24 | pop_t(i).MFObj = inf(1, Prob.T); 25 | pop_t(i).MFCV = inf(1, Prob.T); 26 | pop_t(i).MFObj(t) = pop_t(i).Obj; 27 | pop_t(i).MFCV(t) = pop_t(i).CV; 28 | end 29 | population = [population, pop_t]; 30 | end 31 | end 32 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Multifactorial/Selection_MF.m: -------------------------------------------------------------------------------- 1 | function population = Selection_MF(population, offspring, Prob) 2 | %% Elite selection based on scalar fitness 3 | % Input: population (old), offspring, 4 | % Output: population (new) 5 | 6 | %------------------------------- Copyright -------------------------------- 7 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 8 | % purposes. All publications which use this platform should acknowledge 9 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 10 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 11 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 12 | %-------------------------------------------------------------------------- 13 | 14 | population = [population, offspring]; 15 | 16 | for t = 1:Prob.T 17 | for i = 1:length(population) 18 | Obj(i, 1) = population(i).MFObj(t); 19 | CV(i, 1) = population(i).MFCV(t); 20 | end 21 | [~, rank] = sortrows([CV, Obj], [1, 2]); 22 | for i = 1:length(population) 23 | population(rank(i)).MFRank(t) = i; 24 | end 25 | end 26 | 27 | for i = 1:length(population) 28 | fit(i) = 1 / min([population(i).MFRank]); 29 | end 30 | 31 | [~, rank] = sort(fit, 'descend'); 32 | population = population(rank(1:Prob.N * Prob.T)); 33 | end 34 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Operator/DE_Crossover.m: -------------------------------------------------------------------------------- 1 | function OffDec = DE_Crossover(OffDec, ParDec, CR) 2 | replace = rand(1, size(OffDec, 2)) > CR; 3 | replace(randi(size(OffDec, 2))) = false; 4 | OffDec(:, replace) = ParDec(:, replace); 5 | end 6 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Operator/DE_Crossover_Exp.m: -------------------------------------------------------------------------------- 1 | function OffDec = DE_Crossover_Exp(OffDec, ParDec, CR) 2 | D = size(OffDec, 2); 3 | L = 1 + fix(size(OffDec, 2) * rand()); 4 | replace = L; 5 | position = L; 6 | while rand() < CR && length(replace) < D 7 | position = position + 1; 8 | if position <= D 9 | replace(end + 1) = position; 10 | else 11 | replace(end + 1) = mod(position, D); 12 | end 13 | end 14 | Dec_temp = ParDec; 15 | Dec_temp(:, replace) = OffDec(:, replace); 16 | OffDec = Dec_temp; 17 | end 18 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Operator/GA_Crossover.m: -------------------------------------------------------------------------------- 1 | function [OffDec1, OffDec2] = GA_Crossover(ParDec1, ParDec2, mu) 2 | % SBX - Simulated binary crossover 3 | 4 | D = size(ParDec1, 2); 5 | u = rand(1, D); 6 | beta = zeros(1, D); 7 | beta(u <= 0.5) = (2 * u(u <= 0.5)).^(1 / (mu + 1)); 8 | beta(u > 0.5) = (2 * (1 - u(u > 0.5))).^(-1 / (mu + 1)); 9 | beta = beta .* (-1).^randi([0, 1], 1, D); 10 | beta(rand(1, D) < 0.5) = 1; 11 | 12 | OffDec1 = 0.5 * ((1 + beta) .* ParDec1 + (1 - beta) .* ParDec2); 13 | OffDec2 = 0.5 * ((1 + beta) .* ParDec2 + (1 - beta) .* ParDec1); 14 | end 15 | -------------------------------------------------------------------------------- /DEORA_code/Algorithms/Utils/Operator/GA_Mutation.m: -------------------------------------------------------------------------------- 1 | function Dec = GA_Mutation(Dec, mu) 2 | % Polynomial mutation 3 | 4 | D = size(Dec, 2); 5 | for d = 1:D 6 | if rand() < 1 / D 7 | u = rand(1); 8 | if u <= 0.5 9 | delta = ((2 * u + (1 - 2 * u) * (1 - Dec(d))^(mu + 1)))^(1 / (mu + 1)) - 1; 10 | Dec(d) = Dec(d) + delta; 11 | else 12 | delta = 1 - (2 * (1 - u) + 2 * (u - 0.5) * Dec(d)^(mu + 1))^(1 / (mu + 1)); 13 | Dec(d) = Dec(d) + delta; 14 | end 15 | end 16 | end 17 | end 18 | -------------------------------------------------------------------------------- /DEORA_code/GUI/Utils/MakeGenEqual.m: -------------------------------------------------------------------------------- 1 | function Results = MakeGenEqual(Results) 2 | % Make Results Gen Equal for All Reps 3 | 4 | for prob = 1:size(Results, 1) 5 | for algo = 1:size(Results, 2) 6 | maxGen = 0; 7 | for rep = 1:size(Results, 3) 8 | Gen = size(Results(prob, algo, rep).Obj, 2); 9 | if maxGen < Gen 10 | maxGen = Gen; 11 | end 12 | end 13 | 14 | for rep = 1:size(Results, 3) 15 | Gen = size(Results(prob, algo, rep).Obj, 2); 16 | if Gen < maxGen 17 | for g = Gen + 1:maxGen 18 | Results(prob, algo, rep).Obj(:, g) = Results(prob, algo, rep).Obj(:, Gen); 19 | Results(prob, algo, rep).CV(:, g) = Results(prob, algo, rep).CV(:, Gen); 20 | if isfield(Results(prob, algo, rep), 'Dec') 21 | Results(prob, algo, rep).Obj(:, g) = Results(prob, algo, rep).Obj(:, Gen); 22 | end 23 | end 24 | end 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Metrics/Run_Time.m: -------------------------------------------------------------------------------- 1 | function result = Run_Time(MTOData, varargin) 2 | % 3 | 4 | % Run Time 5 | 6 | %------------------------------- Copyright -------------------------------- 7 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 8 | % purposes. All publications which use this platform should acknowledge 9 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 10 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 11 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 12 | %-------------------------------------------------------------------------- 13 | 14 | result.Metric = 'Min'; 15 | result.RowName = {MTOData.Problems.Name}; 16 | result.ColumnName = {MTOData.Algorithms.Name}; 17 | % Data for Table 18 | result.TableData = []; 19 | 20 | for prob = 1:length(MTOData.Problems) 21 | for algo = 1:length(MTOData.Algorithms) 22 | result.TableData(prob, algo, :) = MTOData.RunTimes(prob, algo, :); 23 | end 24 | end 25 | end 26 | -------------------------------------------------------------------------------- /DEORA_code/Metrics/Utils/getIGD.m: -------------------------------------------------------------------------------- 1 | function score = getIGD(PopObj, optimum) 2 | 3 | % This code is copy from PlatEMO(https://github.com/BIMK/PlatEMO). 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 BIMK Group. You are free to use the PlatEMO for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "PlatEMO" and reference "Ye 8 | % Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform 9 | % for evolutionary multi-objective optimization [educational forum], IEEE 10 | % Computational Intelligence Magazine, 2017, 12(4): 73-87". 11 | %-------------------------------------------------------------------------- 12 | 13 | if size(PopObj, 2) ~= size(optimum, 2) 14 | score = nan; 15 | else 16 | score = mean(min(pdist2(optimum, PopObj), [], 2)); 17 | end 18 | end 19 | -------------------------------------------------------------------------------- /DEORA_code/Metrics/Utils/getIGDp.m: -------------------------------------------------------------------------------- 1 | function score = getIGDp(PopObj, optimum) 2 | 3 | % This code is copy from PlatEMO(https://github.com/BIMK/PlatEMO). 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 BIMK Group. You are free to use the PlatEMO for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "PlatEMO" and reference "Ye 8 | % Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform 9 | % for evolutionary multi-objective optimization [educational forum], IEEE 10 | % Computational Intelligence Magazine, 2017, 12(4): 73-87". 11 | %-------------------------------------------------------------------------- 12 | 13 | if size(PopObj, 2) ~= size(optimum, 2) 14 | score = nan; 15 | else 16 | [Nr, M] = size(optimum); 17 | [N, ~] = size(PopObj); 18 | delta = zeros(Nr, 1); 19 | for i = 1:Nr 20 | delta(i) = min(sqrt(sum(max(PopObj - repmat(optimum(i, :), N, 1), zeros(N, M)).^2, 2))); 21 | end 22 | score = mean(delta); 23 | end 24 | end 25 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Base/Ackley.m: -------------------------------------------------------------------------------- 1 | function [Obj, Con] = Ackley(var, M, opt, g) 2 | % Ackley function 3 | % - var: design variable vector 4 | % - M: rotation matrix 5 | % - opt: shift vector 6 | % - g: Objective value move 7 | 8 | %------------------------------- Copyright -------------------------------- 9 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 10 | % purposes. All publications which use this platform should acknowledge 11 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 12 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 13 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 14 | %-------------------------------------------------------------------------- 15 | 16 | [ps, D] = size(var); 17 | 18 | if size(M, 1) == 1 19 | M = M * eye(D); 20 | end 21 | if size(opt, 2) == 1 22 | opt = opt * ones(1, D); 23 | end 24 | 25 | var = (M(1:D, 1:D) * (var - repmat(opt(1:D), ps, 1))')'; 26 | 27 | sum1 = zeros(ps, 1); 28 | sum2 = zeros(ps, 1); 29 | 30 | for i = 1:D 31 | sum1 = sum1 + var(:, i) .* var(:, i); 32 | sum2 = sum2 + cos(2 * pi .* var(:, i)); 33 | end 34 | 35 | avgsum1 = sum1 ./ D; 36 | avgsum2 = sum2 ./ D; 37 | 38 | Obj = -20 * exp(-0.2 .* sqrt(avgsum1)) - exp(avgsum2) + 20 + exp(1); 39 | Obj = Obj + g; 40 | 41 | Con = zeros(ps, 1); 42 | end 43 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Base/Elliptic.m: -------------------------------------------------------------------------------- 1 | function [Obj, Con] = Elliptic(var, M, opt, g) 2 | % Elliptic function 3 | % - var: design variable vector 4 | % - M: rotation matrix 5 | % - opt: shift vector 6 | % - g: Objective value move 7 | 8 | %------------------------------- Copyright -------------------------------- 9 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 10 | % purposes. All publications which use this platform should acknowledge 11 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 12 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 13 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 14 | %-------------------------------------------------------------------------- 15 | 16 | [ps, D] = size(var); 17 | 18 | if size(M, 1) == 1 19 | M = M * eye(D); 20 | end 21 | if size(opt, 2) == 1 22 | opt = opt * ones(1, D); 23 | end 24 | 25 | var = (M(1:D, 1:D) * (var - repmat(opt(1:D), ps, 1))')'; 26 | 27 | a = 1e+6; 28 | Obj = zeros(ps, 1); 29 | for i = 1:D 30 | Obj = Obj + a.^((i - 1) / (D - 1)) .* var(:, i).^2; 31 | end 32 | Obj = Obj + g; 33 | 34 | Con = zeros(ps, 1); 35 | end 36 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Base/Griewank.m: -------------------------------------------------------------------------------- 1 | function [Obj, Con] = Griewank(var, M, opt, g) 2 | % GRIEWANK function 3 | % - var: design variable vector 4 | % - M: rotation matrix 5 | % - opt: shift vector 6 | % - g: Objective value move 7 | 8 | %------------------------------- Copyright -------------------------------- 9 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 10 | % purposes. All publications which use this platform should acknowledge 11 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 12 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 13 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 14 | %-------------------------------------------------------------------------- 15 | 16 | [ps, D] = size(var); 17 | 18 | if size(M, 1) == 1 19 | M = M * eye(D); 20 | end 21 | if size(opt, 2) == 1 22 | opt = opt * ones(1, D); 23 | end 24 | 25 | var = (M(1:D, 1:D) * (var - repmat(opt(1:D), ps, 1))')'; 26 | 27 | sum1 = zeros(ps, 1); 28 | sum2 = ones(ps, 1); 29 | 30 | for i = 1:D 31 | sum1 = sum1 + var(:, i) .* var(:, i); 32 | sum2 = sum2 .* cos(var(:, i) ./ (sqrt(i))); 33 | end 34 | 35 | Obj = 1 +1/4000 * sum1 - sum2; 36 | Obj = Obj + g; 37 | 38 | Con = zeros(ps, 1); 39 | end 40 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Base/Rastrigin.m: -------------------------------------------------------------------------------- 1 | function [Obj, Con] = Rastrigin(var, M, opt, g) 2 | % Rastrigin function 3 | % - var: design variable vector 4 | % - M: rotation matrix 5 | % - opt: shift vector 6 | % - g: Objective value move 7 | 8 | %------------------------------- Copyright -------------------------------- 9 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 10 | % purposes. All publications which use this platform should acknowledge 11 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 12 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 13 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 14 | %-------------------------------------------------------------------------- 15 | 16 | [ps, D] = size(var); 17 | 18 | if size(M, 1) == 1 19 | M = M * eye(D); 20 | end 21 | if size(opt, 2) == 1 22 | opt = opt * ones(1, D); 23 | end 24 | 25 | var = (M(1:D, 1:D) * (var - repmat(opt(1:D), ps, 1))')'; 26 | 27 | Obj = repmat(10 * D, ps, 1); 28 | 29 | for i = 1:D 30 | Obj = Obj + (var(:, i).^2 - 10 * (cos(2 * pi * var(:, i)))); 31 | end 32 | Obj = Obj + g; 33 | 34 | Con = zeros(ps, 1); 35 | end 36 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Base/Rosenbrock.m: -------------------------------------------------------------------------------- 1 | function [Obj, Con] = Rosenbrock(var, M, opt, g) 2 | % ROSENBROCK function 3 | % - var: design variable vector 4 | % - M: rotation matrix 5 | % - opt: shift vector 6 | % - g: Objective value move 7 | 8 | %------------------------------- Copyright -------------------------------- 9 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 10 | % purposes. All publications which use this platform should acknowledge 11 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 12 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 13 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 14 | %-------------------------------------------------------------------------- 15 | 16 | [ps, D] = size(var); 17 | 18 | if size(M, 1) == 1 19 | M = M * eye(D); 20 | end 21 | if size(opt, 2) == 1 22 | opt = opt * ones(1, D); 23 | end 24 | 25 | var = (M(1:D, 1:D) * (var - repmat(opt(1:D), ps, 1))')'; 26 | 27 | sum1 = zeros(ps, 1); 28 | for ii = 1:(D - 1) 29 | xi = var(:, ii); 30 | xnext = var(:, ii + 1); 31 | new = 100 * (xnext - xi.^2).^2 + (xi - 1).^2; 32 | sum1 = sum1 + new; 33 | end 34 | if D == 1 35 | sum1 = 100 * (var(:, 1) - var(:, 1).^2).^2 + (var(:, 1) - 1).^2; 36 | end 37 | Obj = sum1; 38 | Obj = Obj + g; 39 | 40 | Con = zeros(ps, 1); 41 | end 42 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Base/Schwefel.m: -------------------------------------------------------------------------------- 1 | function [Obj, Con] = Schwefel(var, M, opt, g) 2 | % SCHWEFEL function 3 | % - var: design variable vector 4 | % - M: rotation matrix 5 | % - opt: shift vector 6 | % - g: Objective value move 7 | 8 | %------------------------------- Copyright -------------------------------- 9 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 10 | % purposes. All publications which use this platform should acknowledge 11 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 12 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 13 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 14 | %-------------------------------------------------------------------------- 15 | 16 | [ps, D] = size(var); 17 | 18 | if size(M, 1) == 1 19 | M = M * eye(D); 20 | end 21 | if size(opt, 2) == 1 22 | opt = opt * ones(1, D); 23 | end 24 | 25 | var = (M(1:D, 1:D) * (var - repmat(opt(1:D), ps, 1))')'; 26 | 27 | sum1 = zeros(ps, 1); 28 | 29 | for i = 1:D 30 | sum1 = sum1 + var(:, i) .* sin(sqrt(abs(var(:, i)))); 31 | end 32 | 33 | Obj = 418.9829 * D - sum1; 34 | Obj = Obj + g; 35 | 36 | Con = zeros(ps, 1); 37 | end 38 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Base/Schwefel2.m: -------------------------------------------------------------------------------- 1 | function [Obj, Con] = Schwefel2(var, M, opt, g) 2 | % SCHWEFEL function problem1.2 3 | % - var: design variable vector 4 | % - M: rotation matrix 5 | % - opt: shift vector 6 | % - g: Objective value move 7 | 8 | %------------------------------- Copyright -------------------------------- 9 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 10 | % purposes. All publications which use this platform should acknowledge 11 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 12 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 13 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 14 | %-------------------------------------------------------------------------- 15 | 16 | [ps, D] = size(var); 17 | 18 | if size(M, 1) == 1 19 | M = M * eye(D); 20 | end 21 | if size(opt, 2) == 1 22 | opt = opt * ones(1, D); 23 | end 24 | 25 | var = (M(1:D, 1:D) * (var - repmat(opt(1:D), ps, 1))')'; 26 | 27 | Obj = zeros(ps, 1); 28 | for i = 1:D 29 | Obj = Obj + sum(var(:, 1:i), 2).^2; 30 | end 31 | 32 | Obj = Obj + g; 33 | 34 | Con = zeros(ps, 1); 35 | end 36 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Base/Sphere.m: -------------------------------------------------------------------------------- 1 | function [Obj, Con] = Sphere(var, M, opt, g) 2 | % Sphere function 3 | % - var: design variable vector 4 | % - M: rotation matrix 5 | % - opt: shift vector 6 | % - g: Objective value move 7 | 8 | %------------------------------- Copyright -------------------------------- 9 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 10 | % purposes. All publications which use this platform should acknowledge 11 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 12 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 13 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 14 | %-------------------------------------------------------------------------- 15 | 16 | [ps, D] = size(var); 17 | 18 | if size(M, 1) == 1 19 | M = M * eye(D); 20 | end 21 | if size(opt, 2) == 1 22 | opt = opt * ones(1, D); 23 | end 24 | 25 | var = (M(1:D, 1:D) * (var - repmat(opt(1:D), ps, 1))')'; 26 | 27 | Obj = sum(var.^2, 2); 28 | Obj = Obj + g; 29 | 30 | Con = zeros(ps, 1); 31 | end 32 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Base/Weierstrass.m: -------------------------------------------------------------------------------- 1 | function [Obj, Con] = Weierstrass(var, M, opt, g) 2 | % WEIERSTASS function 3 | % - var: design variable vector 4 | % - M: rotation matrix 5 | % - opt: shift vector 6 | % - g: Objective value move 7 | 8 | %------------------------------- Copyright -------------------------------- 9 | % Copyright (c) Yanchi Li. You are free to use the MToP for research 10 | % purposes. All publications which use this platform should acknowledge 11 | % the use of "MToP" or "MTO-Platform" and cite as "Y. Li, W. Gong, F. Ming, 12 | % T. Zhang, S. Li, and Q. Gu, MToP: A MATLAB Optimization Platform for 13 | % Evolutionary Multitasking, 2023, arXiv:2312.08134" 14 | %-------------------------------------------------------------------------- 15 | 16 | [ps, D] = size(var); 17 | 18 | if size(M, 1) == 1 19 | M = M * eye(D); 20 | end 21 | if size(opt, 2) == 1 22 | opt = opt * ones(1, D); 23 | end 24 | 25 | var = (M(1:D, 1:D) * (var - repmat(opt(1:D), ps, 1))')'; 26 | 27 | a = 0.5; 28 | b = 3; 29 | kmax = 20; 30 | Obj = zeros(ps, 1); 31 | 32 | for i = 1:D 33 | for k = 0:kmax 34 | Obj = Obj + a^k * cos(2 * pi * b^k * (var(:, i) + 0.5)); 35 | end 36 | end 37 | 38 | for k = 0:kmax 39 | Obj = Obj - D * a^k * cos(2 * pi * b^k * 0.5); 40 | end 41 | Obj = Obj + g; 42 | 43 | Con = zeros(ps, 1); 44 | end 45 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/CEC17_MTSO1_CI_HS.m: -------------------------------------------------------------------------------- 1 | classdef CEC17_MTSO1_CI_HS < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = CEC17_MTSO1_CI_HS(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 1000 * 100; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_CEC17_MTSO(1); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/CEC17_MTSO2_CI_MS.m: -------------------------------------------------------------------------------- 1 | classdef CEC17_MTSO2_CI_MS < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = CEC17_MTSO2_CI_MS(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 1000 * 100; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_CEC17_MTSO(2); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/CEC17_MTSO3_CI_LS.m: -------------------------------------------------------------------------------- 1 | classdef CEC17_MTSO3_CI_LS < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = CEC17_MTSO3_CI_LS(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 1000 * 100; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_CEC17_MTSO(3); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/CEC17_MTSO4_PI_HS.m: -------------------------------------------------------------------------------- 1 | classdef CEC17_MTSO4_PI_HS < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = CEC17_MTSO4_PI_HS(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 1000 * 100; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_CEC17_MTSO(4); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/CEC17_MTSO5_PI_MS.m: -------------------------------------------------------------------------------- 1 | classdef CEC17_MTSO5_PI_MS < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = CEC17_MTSO5_PI_MS(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 1000 * 100; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_CEC17_MTSO(5); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/CEC17_MTSO6_PI_LS.m: -------------------------------------------------------------------------------- 1 | classdef CEC17_MTSO6_PI_LS < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = CEC17_MTSO6_PI_LS(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 1000 * 100; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_CEC17_MTSO(6); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/CEC17_MTSO7_NI_HS.m: -------------------------------------------------------------------------------- 1 | classdef CEC17_MTSO7_NI_HS < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = CEC17_MTSO7_NI_HS(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 1000 * 100; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_CEC17_MTSO(7); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/CEC17_MTSO8_NI_MS.m: -------------------------------------------------------------------------------- 1 | classdef CEC17_MTSO8_NI_MS < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = CEC17_MTSO8_NI_MS(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 1000 * 100; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_CEC17_MTSO(8); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/CEC17_MTSO9_NI_LS.m: -------------------------------------------------------------------------------- 1 | classdef CEC17_MTSO9_NI_LS < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = CEC17_MTSO9_NI_LS(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 1000 * 100; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_CEC17_MTSO(9); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/CI_H.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/CI_H.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/CI_L.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/CI_L.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/CI_M.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/CI_M.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/NI_H.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/NI_H.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/NI_L.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/NI_L.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/NI_M.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/NI_M.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/PI_H.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/PI_H.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/PI_L.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/PI_L.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/PI_M.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/CEC17-MTSO/Tasks/PI_M.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case1/C2TOP_Case1_P1.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case1_P1 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case1_P1(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(1, 1); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case1/C2TOP_Case1_P2.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case1_P2 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case1_P2(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(2, 1); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case1/C2TOP_Case1_P3.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case1_P3 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case1_P3(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(3, 1); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case1/C2TOP_Case1_P4.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case1_P4 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case1_P4(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(4, 1); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case1/C2TOP_Case1_P5.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case1_P5 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case1_P5(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(5, 1); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case1/C2TOP_Case1_P6.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case1_P6 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case1_P6(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(6, 1); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case1/C2TOP_Case1_P7.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case1_P7 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case1_P7(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(7, 1); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case1/C2TOP_Case1_P8.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case1_P8 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case1_P8(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(8, 1); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case1/C2TOP_Case1_P9.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case1_P9 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case1_P9(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(9, 1); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case2/C2TOP_Case2_P1.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case2_P1 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case2_P1(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(1, 2); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case2/C2TOP_Case2_P2.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case2_P2 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case2_P2(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(2, 2); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case2/C2TOP_Case2_P3.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case2_P3 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case2_P3(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(3, 2); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case2/C2TOP_Case2_P4.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case2_P4 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case2_P4(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(4, 2); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case2/C2TOP_Case2_P5.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case2_P5 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case2_P5(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(5, 2); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case2/C2TOP_Case2_P6.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case2_P6 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case2_P6(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(6, 2); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case2/C2TOP_Case2_P7.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case2_P7 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case2_P7(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(7, 2); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case3/C2TOP_Case3_P1.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case3_P1 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case3_P1(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(1, 3); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case3/C2TOP_Case3_P2.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case3_P2 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case3_P2(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(2, 3); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case3/C2TOP_Case3_P3.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case3_P3 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case3_P3(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(3, 3); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case3/C2TOP_Case3_P4.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case3_P4 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case3_P4(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(4, 3); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case3/C2TOP_Case3_P5.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case3_P5 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case3_P5(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(5, 3); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case3/C2TOP_Case3_P6.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case3_P6 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case3_P6(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(6, 3); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case3/C2TOP_Case3_P7.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case3_P7 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case3_P7(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(7, 3); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case3/C2TOP_Case3_P8.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case3_P8 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case3_P8(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(8, 3); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case3/C2TOP_Case3_P9.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case3_P9 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case3_P9(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(9, 3); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case4/C2TOP_Case4_P1.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case4_P1 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case4_P1(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(1, 4); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case4/C2TOP_Case4_P2.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case4_P2 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case4_P2(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(2, 4); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case4/C2TOP_Case4_P3.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case4_P3 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case4_P3(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(3, 4); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case4/C2TOP_Case4_P4.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case4_P4 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case4_P4(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(4, 4); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case4/C2TOP_Case4_P5.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case4_P5 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case4_P5(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(5, 4); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case4/C2TOP_Case4_P6.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case4_P6 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case4_P6(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(6, 4); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case4/C2TOP_Case4_P7.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case4_P7 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case4_P7(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(7, 4); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case4/C2TOP_Case4_P8.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case4_P8 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case4_P8(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(8, 4); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C2TOP/Case4/C2TOP_Case4_P9.m: -------------------------------------------------------------------------------- 1 | classdef C2TOP_Case4_P9 < Problem 2 | % 3 | 4 | methods 5 | function Prob = C2TOP_Case4_P9(varargin) 6 | Prob = Prob@Problem(varargin); 7 | Prob.maxFE = 1000 * 100 * 2; 8 | end 9 | 10 | function setTasks(Prob) 11 | Tasks = benchmark_CEC17_MTSO_Competitive(9, 4); 12 | Prob.T = length(Tasks); 13 | for t = 1:Prob.T 14 | Prob.D(t) = Tasks(t).Dim; 15 | Prob.Fnc{t} = Tasks(t).Fnc; 16 | Prob.Lb{t} = Tasks(t).Lb; 17 | Prob.Ub{t} = Tasks(t).Ub; 18 | end 19 | end 20 | end 21 | end 22 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP1.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP1 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP1(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(1); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP10.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP10 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP10(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(10); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP11.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP11 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP11(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(11); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP12.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP12 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP12(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(12); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP13.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP13 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP13(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(13); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP14.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP14 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP14(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(14); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP15.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP15 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP15(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(15); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP2.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP2 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP2(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(2); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP3.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP3 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP3(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(3); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP4.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP4 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP4(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(4); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP5.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP5 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP5(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(5); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP6.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP6 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP6(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(6); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP7.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP7 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP7(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(7); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP8.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP8 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP8(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(8); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/C4TOP9.m: -------------------------------------------------------------------------------- 1 | classdef C4TOP9 < Problem 2 | % 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2022 Yanchi Li. You are free to use the MTO-Platform for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "MTO-Platform" and cite 8 | % or footnote "https://github.com/intLyc/MTO-Platform" 9 | %-------------------------------------------------------------------------- 10 | 11 | methods 12 | function Prob = C4TOP9(varargin) 13 | Prob = Prob@Problem(varargin); 14 | Prob.maxFE = 500 * 100 * 4; 15 | end 16 | 17 | function setTasks(Prob) 18 | Tasks = benchmark_C4TOP(9); 19 | Prob.T = length(Tasks); 20 | for t = 1:Prob.T 21 | Prob.D(t) = Tasks(t).Dim; 22 | Prob.Fnc{t} = Tasks(t).Fnc; 23 | Prob.Lb{t} = Tasks(t).Lb; 24 | Prob.Ub{t} = Tasks(t).Ub; 25 | end 26 | end 27 | end 28 | end 29 | -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/Data/M_matrix.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/Competitive-C4TOP/Data/M_matrix.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Multi-task/Competitive-C4TOP/Data/O_matrix.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Multi-task/Competitive-C4TOP/Data/O_matrix.mat -------------------------------------------------------------------------------- /DEORA_code/Problems/Real-world Applications/Sensor Coverage Problem/SCP_Adata.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/DEORA_code/Problems/Real-world Applications/Sensor Coverage Problem/SCP_Adata.mat -------------------------------------------------------------------------------- /DEORA_code/README.md: -------------------------------------------------------------------------------- 1 | Li G, Zhang Q, Wang Z. Evolutionary competitive multitasking optimization[J]. IEEE Transactions on Evolutionary Computation, 2022, 26(2): 278-289. 2 | 3 | 4 | This code is implemented in the MTO-Platform (https://github.com/intLyc/MTO-Platform). 5 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/__MACOSX/._release: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/__MACOSX/._release -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/__MACOSX/release/._.DS_Store: -------------------------------------------------------------------------------- 1 | Mac OS X  2Fx ATTRxx -------------------------------------------------------------------------------- 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4 | HV_SRCS = hv.c 5 | HV_HDRS = hv.h 6 | HV_OBJS = $(HV_SRCS:.c=.o) 7 | HV_LIB = fpli_hv.a 8 | 9 | $(HV_LIB): $(HV_OBJS) 10 | @$(RM) $@ 11 | $(QUIET_AR)$(AR) rcs $@ $^ 12 | 13 | ## Augment CFLAGS for hv.[co] objects 14 | hv.o: CPPFLAGS += -D VARIANT=$(VARIANT) 15 | 16 | ## Dependencies: 17 | $(HV_OBJS): $(HV_HDRS) 18 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/R_metric.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/R_metric.m -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/filter_NDS.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/filter_NDS.m -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/filter_PF.m: -------------------------------------------------------------------------------- 1 | function filtered_pf = filter_PF(PF,w_point, ref_point, radius) 2 | min_d = Inf; 3 | filtered_pf = []; 4 | d1 = norm(w_point - ref_point); 5 | for i = 1 : size(PF,1) 6 | d2 = norm(PF(i,:) - ref_point); 7 | d3 = norm(PF(i,:) - w_point); 8 | p = 0.5 * (d1 + d2 + d3); 9 | d = 2 * (p*(p-d1)*(p-d2)*(p-d3))^0.5 / d1; 10 | if d < min_d 11 | min_d = d; 12 | pf_center = PF(i,:); 13 | end 14 | end 15 | k = 1; 16 | for i = 1 : size(PF,1) 17 | if norm(PF(i,:)-pf_center) <= radius/2.0 18 | filtered_pf(k,:) = PF(i,:); 19 | k = k + 1; 20 | end 21 | end 22 | end -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/io.h: -------------------------------------------------------------------------------- 1 | #ifndef _HV_IO_H_ 2 | #define _HV_IO_H_ 3 | 4 | #include 5 | #include 6 | #include 7 | 8 | #define point_printf_format "%-16.15g" 9 | 10 | /* If we're not using GNU C, elide __attribute__ */ 11 | #ifndef __GNUC__ 12 | # define __attribute__(x) /* NOTHING */ 13 | #endif 14 | 15 | void 16 | errprintf(const char * template,...) 17 | /* enables the compiler to check the format string against the 18 | parameters */ __attribute__ ((format(printf, 1, 2))); 19 | 20 | void warnprintf(const char *template,...) 21 | /* enables the compiler to check the format string against the 22 | parameters */ __attribute__ ((format(printf, 1, 2))); 23 | 24 | /* Error codes for read_data. */ 25 | #define READ_INPUT_FILE_EMPTY -1 26 | #define READ_INPUT_WRONG_INITIAL_DIM -2 27 | 28 | int 29 | read_data (const char *filename, double **data_p, 30 | int *nobjs_p, int **cumsizes_p, int *nsets_p); 31 | 32 | #endif 33 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/Darwin_i386_cc.mk: -------------------------------------------------------------------------------- 1 | include mk/gcc.mk 2 | 3 | ifdef MARCH 4 | OPT_CFLAGS += -march=$(MARCH) 5 | ARCH := $(MARCH) 6 | else ifeq ($(shell which gcc-mp-4.4 > /dev/null && echo y),y) 7 | CC := gcc-mp-4.4 8 | ARCH := $(gcc-guess-march) 9 | OPT_CFLAGS += -march=$(ARCH) 10 | else ifeq ($(shell which gcc-4.2 > /dev/null && echo y),y) 11 | CC := gcc-4.2 12 | ARCH := native 13 | OPT_CFLAGS += -mtune=$(ARCH) 14 | else 15 | $(warning No processor specific optimizations set. Consider defining OPT_CFLAGS manually.) 16 | endif 17 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/Darwin_i386_gcc-4.2.mk: -------------------------------------------------------------------------------- 1 | include mk/gcc.mk 2 | 3 | ifdef MARCH 4 | ARCH := $(MARCH) 5 | OPT_CFLAGS += -march=$(MARCH) 6 | else 7 | ## gcc-4.2 does not support -march=native on OS X for some reason... 8 | ARCH := native 9 | OPT_CFLAGS += -mtune=$(ARCH) 10 | endif 11 | 12 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/Linux_x86_64_icc.mk: -------------------------------------------------------------------------------- 1 | include mk/icc.mk 2 | 3 | ifdef XARCH 4 | OPT_CFLAGS += -x$(XARCH) 5 | ARCH := $(XARCH) 6 | else 7 | ARCH := native 8 | OPT_CFLAGS += -xHOST 9 | endif 10 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/Linux_x86_64_pathcc.mk: -------------------------------------------------------------------------------- 1 | ## Patchscales compiler is partially based on gccs front-end. It is therefore safe to use 2 | ## the gcc defaults. 3 | 4 | ifndef MARCH 5 | ## auto is for pathscale what native is for gcc: 6 | MARCH=auto 7 | endif 8 | 9 | include mk/gcc.mk 10 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/Linux_x86_64_pgcc.mk: -------------------------------------------------------------------------------- 1 | include mk/pgcc.mk 2 | 3 | ifdef MARCH 4 | ARCH := $(MARCH) 5 | OPT_CFLAGS += -tp=$(ARCH) 6 | else 7 | $(warning Using generic x64 cpu target. Consider setting MARCH to a value given by 'pgcc -tp') 8 | ARCH := x64 9 | OPT_CFLAGS += -tp=$(ARCH) 10 | endif 11 | 12 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/Linux_x86_64_suncc.mk: -------------------------------------------------------------------------------- 1 | include mk/suncc.mk 2 | 3 | ifdef XTARGET 4 | ARCH := $(XTARGET) 5 | OPT_CFLAGS += -xtarget=$(ARCH) 6 | else 7 | ifdef XARCH 8 | ARCH := $(XARCH) 9 | OPT_CFLAGS += -xarch=$(ARCH) 10 | else 11 | ARCH := native 12 | OPT_CFLAGS += -xtarget=$(ARCH) 13 | endif 14 | endif 15 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/README: -------------------------------------------------------------------------------- 1 | ------------------------ 2 | Adding new compilers 3 | ------------------------ 4 | 5 | If you want or need to add a new compiler / architecture combination 6 | to the build system you will need to create a file named 7 | 8 | $(uname -s)_$(uname -m)_.mk 9 | 10 | At a minimum it needs to set the OPT_CFLAGS and ARCH variables. If all 11 | you want to do is use GCC on a new platform you can usually simple 12 | include gcc.mk. In general this should only be necessary if you are 13 | cross-compiling or want to add additional flags after the general gcc 14 | detection logic. 15 | 16 | If your compiler is unknown, the build system will attempt to auto 17 | detect if it is a gcc variant and then use mk/gcc.mk. 18 | 19 | If you have new platform or compiler support files, please submit them 20 | to manuel.lopez-ibanezulb.ac.be. 21 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/cc.mk: -------------------------------------------------------------------------------- 1 | # -*- Makefile-gmake -*- 2 | 3 | CCversion=$(shell $(CC) -v 2>&1) 4 | 5 | ## Is $(CC) a gcc variant? 6 | ifneq (,$(findstring gcc,$(CCversion))) 7 | $(info Detected C compiler to be a GCC variant.) 8 | include mk/gcc.mk 9 | endif 10 | 11 | # Is $(CC) the Sun C compiler? 12 | ifneq (,$(findstring Sun,$(CCversion))) 13 | $(info Detected C compiler to the Sun Studio C compiler.) 14 | include mk/suncc.mk 15 | endif 16 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/icc.mk: -------------------------------------------------------------------------------- 1 | ifeq ($(DEBUG), 0) 2 | OPT_CFLAGS := -O3 -std=c99 -Wall -Wcheck 3 | else 4 | OPT_CFLAGS := -g -std=c99 -Wall -Wcheck 5 | endif 6 | 7 | AR := xiar 8 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/pgcc.mk: -------------------------------------------------------------------------------- 1 | ifeq ($(DEBUG), 0) 2 | OPT_CFLAGS := -fast -O3 -Msmartalloc -c99 3 | else 4 | OPT_CFLAGS := -g -c99 5 | endif 6 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/mk/suncc.mk: -------------------------------------------------------------------------------- 1 | ifeq ($(DEBUG), 0) 2 | OPT_CFLAGS := -fast -xO3 -xc99=all 3 | else 4 | OPT_CFLAGS := -g -xc99=all 5 | endif 6 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/multi_layer_weight.m: -------------------------------------------------------------------------------- 1 | % ------------------------------------------------------------------------% 2 | % This is the function to sample weight vecotrs using multiple-layer method 3 | % 4 | % Author: Dr. Ke Li 5 | % Affliation: CODA Group @ University of Exeter 6 | % Contact: k.li@exeter.ac.uk || https://coda-group.github.io/ 7 | % ------------------------------------------------------------------------% 8 | 9 | function W = multi_layer_weight(objDim, no_layers, no_gaps, shrink_factors) 10 | 11 | layer_sizes = zeros(1, no_layers); 12 | 13 | %% get the number of sample size on each layer 14 | for i = 1 : no_layers 15 | layer_sizes(i) = nchoosek(objDim + no_gaps(i) - 1, no_gaps(i)); 16 | end 17 | 18 | %% weight vectors in the first layer 19 | cur_layer = initweight(objDim, layer_sizes(1)); 20 | W = cur_layer'; 21 | for i = 2 : no_layers 22 | %% generate a temporary layer 23 | temp_layer = initweight(objDim, layer_sizes(i)); 24 | %% shrink the temporary layer (coordinate transformation) 25 | cur_layer = (1 - shrink_factors(i)) / objDim * ones(objDim, layer_sizes(i)) + shrink_factors(i) * temp_layer; 26 | %% incorporate the current layer into the whole weight vector set 27 | W = [W; cur_layer']; 28 | end 29 | end -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/resource.h: -------------------------------------------------------------------------------- 1 | /* 2 | * resource.h 3 | * This file has no copyright assigned and is placed in the Public Domain. 4 | * This file is a part of the mingw-runtime package. 5 | * No warranty is given; refer to the file DISCLAIMER within the package. 6 | * 7 | * Based on: 8 | * http://www.opengroup.org/onlinepubs/000095399/basedefs/sys/resource.h.html 9 | */ 10 | #ifndef _RESOURCE_H_ 11 | #define _RESOURCE_H_ 12 | 13 | #include /* for struct timeval */ 14 | 15 | #define RUSAGE_SELF (1<<0) 16 | #define RUSAGE_CHILDREN (1<<1) 17 | 18 | struct rusage 19 | { 20 | struct timeval ru_utime; /* user time used */ 21 | struct timeval ru_stime; /* system time used */ 22 | }; 23 | 24 | int getrusage(int who, struct rusage * usage); 25 | 26 | #endif /* Not _RESOURCE_H_ */ 27 | -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/svn_version: -------------------------------------------------------------------------------- 1 | 207:208 -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/test_data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/test_data/.DS_Store -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/test_data/RNSGA2/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/test_data/RNSGA2/.DS_Store -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/test_data/r-stm/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/test_data/r-stm/.DS_Store -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/SMS-EMOA/Hypervolume_MEX/release/trim_cubic.m: -------------------------------------------------------------------------------- 1 | % ------------------------------------------------------------------------% 2 | % This function trims the irrelevant solutions for R-metric computation. 3 | % 4 | % Author: Dr. Ke Li 5 | % Affliation: CODA Group @ University of Exeter 6 | % Contact: k.li@exeter.ac.uk || https://coda-group.github.io/ 7 | % ------------------------------------------------------------------------% 8 | 9 | function filtered_pop = trim_cubic(pop, centroid, range) 10 | 11 | [popsize, objDim] = size(pop); 12 | 13 | centroid_matrix = centroid(ones(1, popsize), :); 14 | 15 | diff_matrix = pop - centroid_matrix; 16 | 17 | radius = range / 2.0; 18 | flag_matrix = abs(diff_matrix) < radius; 19 | flag_sum = sum(flag_matrix, 2); 20 | 21 | filtered_idx = flag_sum == objDim; 22 | filtered_pop = pop(filtered_idx, :); 23 | 24 | end -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/e-MOEA/UpdatePopulation.m: -------------------------------------------------------------------------------- 1 | function Population = UpdatePopulation(Population,Offspring) 2 | % Update the population 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2021 BIMK Group. You are free to use the PlatEMO for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "PlatEMO" and reference "Ye 8 | % Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform 9 | % for evolutionary multi-objective optimization [educational forum], IEEE 10 | % Computational Intelligence Magazine, 2017, 12(4): 73-87". 11 | %-------------------------------------------------------------------------- 12 | 13 | N = length(Population); 14 | 15 | %% Insert the offspring into the population by epsilon-dominance 16 | if ~any(all(Population.objs<=repmat(Offspring.obj,N,1),2)) 17 | Dominate = find(all(repmat(Offspring.obj,N,1)<=Population.objs,2)); 18 | if ~isempty(Dominate) 19 | k = randi(length(Dominate)); 20 | Population(Dominate(k)) = Offspring; 21 | else 22 | k = randi(N); 23 | Population(k) = Offspring; 24 | end 25 | end 26 | end -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/mNSGA-II/CrowdingDistance.m: -------------------------------------------------------------------------------- 1 | function CrowdDis = CrowdingDistance(PopObj,FrontNo) 2 | % Calculate the crowding distance of each solution front by front 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2018-2019 BIMK Group. You are free to use the PlatEMO for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "PlatEMO" and reference "Ye 8 | % Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform 9 | % for evolutionary multi-objective optimization [educational forum], IEEE 10 | % Computational Intelligence Magazine, 2017, 12(4): 73-87". 11 | %-------------------------------------------------------------------------- 12 | 13 | [N,M] = size(PopObj); 14 | CrowdDis = zeros(1,N); 15 | Fronts = setdiff(unique(FrontNo),inf); 16 | for f = 1 : length(Fronts) 17 | Front = find(FrontNo==Fronts(f)); 18 | Fmax = max(PopObj(Front,:),[],1); 19 | Fmin = min(PopObj(Front,:),[],1); 20 | for i = 1 : M 21 | [~,Rank] = sortrows(PopObj(Front,i)); 22 | CrowdDis(Front(Rank(1))) = inf; 23 | CrowdDis(Front(Rank(end))) = inf; 24 | for j = 2 : length(Front)-1 25 | CrowdDis(Front(Rank(j))) = CrowdDis(Front(Rank(j)))+(PopObj(Front(Rank(j+1)),i)-PopObj(Front(Rank(j-1)),i))/(Fmax(i)-Fmin(i)); 26 | end 27 | end 28 | end 29 | end -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/ALGORITHM/mNSGA-II/EnvironmentalSelection_modified.m: -------------------------------------------------------------------------------- 1 | function [Population,FrontNo,CrowdDis] = EnvironmentalSelection_modified(Population,N,u) 2 | % The environmental selection of NSGA-II 3 | 4 | %------------------------------- Copyright -------------------------------- 5 | % Copyright (c) 2018-2019 BIMK Group. You are free to use the PlatEMO for 6 | % research purposes. All publications which use this platform or any code 7 | % in the platform should acknowledge the use of "PlatEMO" and reference "Ye 8 | % Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform 9 | % for evolutionary multi-objective optimization [educational forum], IEEE 10 | % Computational Intelligence Magazine, 2017, 12(4): 73-87". 11 | %-------------------------------------------------------------------------- 12 | 13 | %% Non-dominated sorting 14 | [FrontNo,MaxFNo] = NDSort(u,Population.cons,N); 15 | Next = FrontNo < MaxFNo; 16 | 17 | %% Calculate the crowding distance of each solution 18 | CrowdDis = CrowdingDistance(u,FrontNo); 19 | 20 | %% Select the solutions in the last front based on their crowding distances 21 | Last = find(FrontNo==MaxFNo); 22 | [~,Rank] = sort(CrowdDis(Last),'descend'); 23 | Next(Last(Rank(1:N-sum(Next)))) = true; 24 | 25 | %% Population for next generation 26 | Population = Population(Next); 27 | FrontNo = FrontNo(Next); 28 | CrowdDis = CrowdDis(Next); 29 | end -------------------------------------------------------------------------------- /ECPF&DRS_CAIS/README.txt: -------------------------------------------------------------------------------- 1 | All code runs on PLATEMO. 2 | https://github.com/BIMK/PlatEMO 3 | 4 | 5 | 6 | ----------------------------------------------------------NOTICE---------------------------------------------------- 7 | A wrong reference point(1.0, 1.0, 1.0) is adopted in the calculation of Table II of "The dilemma between eliminating dominance resistant solutions and preserving boundary solutions of extremely convex Pareto fronts". This mistake does not change the ranking of the algorithm and the conclusion of the paper. Please correct it to (1.1 1.1 1.1) when you verify the result. We are very sorry for the mistake caused. -------------------------------------------------------------------------------- /IDRCEA-main/DEbest.m: -------------------------------------------------------------------------------- 1 | 2 | % DEoperating consist of mutation and crossover 3 | function [UU] = DEbest(P,Xpbest,F,CR,UB,LB,T) 4 | [NP,Dim] = size(P); 5 | UU=[]; 6 | for jj =1 :T 7 | 8 | for i=1:NP 9 | F1=F(randi([1,length(F)],1,1)); 10 | CR1=CR(randi([1,length(CR)],1,1))'; 11 | % mutation 12 | k0=randi([1,NP]); 13 | while(k0==i) 14 | k0=randi([1,NP]); 15 | end 16 | P1=P(k0,:); 17 | k1=randi([1,NP]); 18 | while(k1==i||k1==k0) 19 | k1=randi([1,NP]); 20 | end 21 | P2=P(k1,:); 22 | % Xpbest = hisx(1,:); 23 | V(i,:)=Xpbest+F1.*(P1-P2); 24 | % bound 25 | for j=1:Dim 26 | if (V(i,j)>UB(i,j)||V(i,j) UB(i,1)); 31 | V(i,inB) = LB(i,inB)+rand*(UB(i,inB)-LB(i,inB)); 32 | 33 | for j=1:Dim 34 | if (V(i,j)>UB(i,j)||V(i,j) VRmax2(1,1)); 10 | offspring(i2, indexB)= VRmin2(i2,indexB)+rand*(VRmax2(i2,indexB)-VRmin2(i2,indexB)); 11 | end 12 | 13 | 14 | end -------------------------------------------------------------------------------- /IDRCEA-main/README.md: -------------------------------------------------------------------------------- 1 | # IDRCEA 2 | Li G, Xie L, Wang Z, et al. Evolutionary algorithm with individual-distribution search strategy and regression-classification surrogates for expensive optimization[J]. Information Sciences, 2023, 634: 423-442 3 | -------------------------------------------------------------------------------- /IDRCEA-main/RUN_IDRCEA.m: -------------------------------------------------------------------------------- 1 | function [gsamp1 ,time_cost] = RUN_IDRCEA(runs, D, FUN, LB, UB, fname,f_bias) 2 | time_begin=tic; 3 | warning('off'); 4 | addpath(genpath(pwd)); 5 | 6 | for r=1:runs 7 | % main loop 8 | fprintf('\n'); 9 | disp(['FUNCTION: ', fname,' RUN: ', num2str(r)]); 10 | fprintf('\n'); 11 | [hisf,mf,gfs]= IDRCEA(FUN,D,LB,UB); 12 | fprintf('Best fitness (PSO-final): %e\n',min(hisf)); 13 | gsamp1(r,:)=gfs(1:mf); 14 | end 15 | 16 | %%%%%%%%%%%%%%%%%%%%% Output options %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 17 | samp_mean = mean(gsamp1(:,end)); 18 | samp_mean_error = samp_mean - f_bias; 19 | std_samp = std(gsamp1(:,end)); 20 | gsamp1_ave = mean(gsamp1,1); 21 | 22 | % Time Complexity 23 | time_cost=toc(time_begin); 24 | save(strcat('result/NFE',num2str(mf),'_',fname,' runs=',num2str(runs),' Dim=',num2str(D))); 25 | end 26 | -------------------------------------------------------------------------------- /IDRCEA-main/my_rbfbuild.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/IDRCEA-main/my_rbfbuild.m -------------------------------------------------------------------------------- /IDRCEA-main/run_myexperiment.m: -------------------------------------------------------------------------------- 1 | 2 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3 | %Code for the IDRCEA 4 | %Copyright (c) 2023 CIAM Group@SDIM SUSTech. 5 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 6 | 7 | clc; 8 | clear all; 9 | addpath(genpath(pwd)); 10 | 11 | % Experiment Parameter 12 | TestFuns = { 'GRIEWANK'; 'ACKLEY'; 'ROSENBROCK'; 'ELLIPSOID'; 'RASTRIGIN';'CEC05_f1'; 'CEC05_f2'; 'CEC05_f3'; 'CEC05_f4'; 'CEC05_f5'; 'CEC05_f6'; 'CEC05_f7'; 13 | 'CEC05_f8'; 'CEC05_f9'; 'CEC05_f10';'CEC05_f11'; 'CEC05_f12'; 'CEC05_f13'; 'CEC05_f14'; 'CEC05_f15';}; 14 | 15 | dims = [10 30 50 100]; % Dimensions 16 | Runs = 20; % Number of runs 17 | 18 | d = size(dims,2); 19 | o = length(TestFuns); 20 | 21 | f_bias_set = [0 0 0 0 0 -450 -450 -450 -450 -310 390 -180 -140 -330 -330 90 -460 -130 -300 120]; 22 | % runs according to dims and objs. 23 | for i = 1:d 24 | for j = 6:o 25 | f_bias = f_bias_set(j); 26 | fname = cell2mat(TestFuns(j)); 27 | FUN=@(x) feval(fname,x); 28 | [Xmin, Xmax] = variable_domain(fname); 29 | LB = repmat((Xmin),1,dims(i)); 30 | UB = repmat((Xmax),1,dims(i),1); 31 | [gsamp1,time_cost] = RUN_IDRCEA(Runs,dims(i),FUN, LB, UB, fname, f_bias); 32 | end 33 | end 34 | save Result -------------------------------------------------------------------------------- /IDRCEA-main/srgtsFitCreateState.m: -------------------------------------------------------------------------------- 1 | function srgtSTT = srgtsFitCreateState(srgtOPT) 2 | % not meaningful for the user 3 | 4 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 5 | % Felipe A. C. Viana 6 | % felipeacviana@gmail.com 7 | % http://sites.google.com/site/felipeacviana 8 | % 9 | % This program is free software; you can redistribute it and/or 10 | % modify it. This program is distributed in the hope that it will be useful, 11 | % but WITHOUT ANY WARRANTY; without even the implied warranty of 12 | % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 13 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 14 | 15 | srgtSTT.FIT_Fn = srgtOPT.FIT_Fn; 16 | srgtSTT.FIT_FnVal = NaN; 17 | 18 | return 19 | -------------------------------------------------------------------------------- /IMOEA-ARP/IMOEA-ARP/Hypervolume_MEX.rar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/IMOEA-ARP/IMOEA-ARP/Hypervolume_MEX.rar -------------------------------------------------------------------------------- /IMOEA-ARP/README.md: -------------------------------------------------------------------------------- 1 | Codes for "Multi-objective Optimization Problem with Hardly Dominated Boundaries: Benchmark, Analysis, and Indicator-based Algorithm". 2 | 3 | IMOEA-ARP is implemented on the PlatEMO platform (version 3.x). Please place the folder "IMOEA-ARP" in the path "PlatEMO\Algorithms\Multi-objective optimization", and folders 4 | "SHDB-MOP" and "Rectangle Problem" in the path "PlatEMO\Problems\Multi-objective optimization". 5 | 6 | After placing the aforementioned folders in the specified paths, please unzip the 7 | "Hypervolume_MEX.rar" in the folder "IMOEA-ARP", and the destination path for unzipped files is "PlatEMO\Algorithms\Multi-objective optimization\IMOEA-ARP". 8 | -------------------------------------------------------------------------------- /MOEA-D-ACN/README.md: -------------------------------------------------------------------------------- 1 | Codes for "Decomposition with Adaptive Composite Norm for Evolutionary Multi-Objective Combinatorial Optimization". 2 | 3 | MOEA/D-ACN is implemented on the PlatEMO platform (version 3.x). Please place the folder in the path "PlatEMO\Algorithms\Multi-objective optimization". 4 | 5 | --- 6 | 7 | See [here](https://github.com/EricZheng1024/MOEA-D-ACN) for details. 8 | -------------------------------------------------------------------------------- /MOEA-ESD/README.md: -------------------------------------------------------------------------------- 1 | Codes for "Multi-objective Evolutionary Algorithm with Evolutionary-status-driven Environmental Selection". 2 | 3 | MOEA-ESD is implemented on the PlatEMO platform (version 3.x). Please place the folder "MOEA-ESD" in the path "PlatEMO\Algorithms\Multi-objective optimization", and place folders 4 | "mDTLZ" and "mUF" in the path "PlatEMO\Problems\Multi-objective optimization". 5 | -------------------------------------------------------------------------------- /MOEAD-ASS_Code/MOEAD-ASS.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/MOEAD-ASS_Code/MOEAD-ASS.zip -------------------------------------------------------------------------------- /MOEAD-ASS_Code/README.md: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2023 CIAM Group 2 | **The code can only be used for non-commercial purposes. Please contact the authors if you want to use this code for other purposes.** 3 | **All publications using this code should properly cite the corresponding paper:
** 4 | **Zhenkun Wang, Qingfu Zhang, Yew-Soon Ong, Shunyu Yao, Haitao Liu, Jianping Luo, Choose Appropriate Subproblems for Collaborative Modeling in Expensive Multiobjective Optimization, IEEE Transactions on Cybernetics, 53(1): 483-496, 2023.** 5 | **** 6 | The codes of MOEA/D-ASS for expensive multi-objective optimization problems 7 | -------------------------------------------------------------------------------- /MOEAD-RD/MOEAD_MD/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/MOEAD-RD/MOEAD_MD/__init__.py -------------------------------------------------------------------------------- /MOEAD-RD/MOEAD_MD/my_io.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | 5 | def join_path(*args): 6 | return os.path.join(*args) 7 | 8 | 9 | def read_input_file(file_path): 10 | with open(file_path, 'r', encoding='utf-8', errors='ignore') as f: 11 | message_str = f.read() 12 | return json.loads(message_str) 13 | 14 | 15 | def write_output_file(data, file_path): 16 | file_dir = os.path.dirname(file_path) 17 | if not os.path.exists(file_dir): 18 | os.mkdir(file_dir) 19 | with open(file_path, 'w', encoding='utf-8', errors='ignore') as f: 20 | json.dump(data, f, ensure_ascii=False, indent=4) 21 | 22 | def write_pareto(pareto, output_dir, file_name): 23 | ans = {"estimateCode": file_name, "solutionArray": []} 24 | for one in pareto: 25 | ans["solutionArray"].append(one.res_truck_list) 26 | write_output_file(ans, join_path(output_dir, file_name)) 27 | -------------------------------------------------------------------------------- /MOEAD-RD/MOEAD_MD/routing.py: -------------------------------------------------------------------------------- 1 | import random 2 | 3 | 4 | def get_random_sol(platfrom_list): 5 | sol = platfrom_list[:] 6 | random.shuffle(sol) 7 | return sol 8 | 9 | 10 | def get_sub_sol(sol, must_visted_dict): 11 | """ 12 | param: sol: ["platCode", ...] 13 | must_visted_dict: {"platCode": True/False, ...} 14 | return: subsol: [["platCode", ...], ...] 15 | """ 16 | sub_sols = [] 17 | end_pointer = len(sol) 18 | start_pointer = len(sol) 19 | for platform in reversed(sol): 20 | start_pointer -= 1 21 | if must_visted_dict[platform]: 22 | sub_sols.append([x for x in sol[start_pointer:end_pointer]]) 23 | end_pointer = start_pointer 24 | if must_visted_dict[sol[0]] is False: 25 | sub_sols.append([x for x in sol[0:end_pointer]]) 26 | return sub_sols 27 | -------------------------------------------------------------------------------- /MOEAD-RD/README.md: -------------------------------------------------------------------------------- 1 | # MOEAD-RD 2 | Li, H., Li, G., Jiang, Q., Wang, J., & Wang, Z. (2024). MOEA/D with customized replacement neighborhood and dynamic resource allocation for solving 3L-SDHVRP. Swarm and Evolutionary Computation, 101463. https://doi.org/10.1016/j.swevo.2023.101463 3 | 4 | ## Run 5 | 6 | ```shell 7 | mkdir EvolutionaryAlgorithm_Codes && cd EvolutionaryAlgorithm_Codes 8 | git init 9 | git remote add origin https://github.com/CIAM-Group/EvolutionaryAlgorithm_Codes.git 10 | git config core.sparseCheckout true 11 | echo "MOEAD-RD/*" > .git/info/sparse-checkout 12 | git pull origin main 13 | cd MOEAD-RD/ 14 | 15 | python run.py 16 | ``` 17 | -------------------------------------------------------------------------------- /MOEAD-RD/global_var.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | list_gener_use_times, list_eval_use_times, list_init_use_times = [], [], [] 6 | gener_use_times, eval_use_times, init_use_times = [], [], [] 7 | -------------------------------------------------------------------------------- /MTOTC/MTOTC_codes.rar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/MTOTC/MTOTC_codes.rar -------------------------------------------------------------------------------- /MTOTC/readme.md: -------------------------------------------------------------------------------- 1 | Source code of the paper： Li, G., Wang, Z., Gao, W., & Wang, L. (2024). Decoupling Constraint: Task Clone-Based Multi-Tasking Optimization for Constrained Multi-Objective Optimization. IEEE Transactions on Evolutionary Computation. 2 | -------------------------------------------------------------------------------- /On-GMMOR/GMMOR.m: -------------------------------------------------------------------------------- 1 | function [K,Fs,P]=GMMOR(Population,All_Fs, Lambda,Kmax) 2 | [IDx,~]=GMM_cluster(Population.objs,Kmax); 3 | % The number of Clusters 4 | K=max(IDx) ; 5 | Objectives=Population.objs; 6 | Objectives=Objectives(:,All_Fs); 7 | for k=1:K 8 | new_Fs{k}=LHA(Objectives(IDx==k,:),Lambda); 9 | Fs{k}=All_Fs(new_Fs{k}); 10 | P{k}=Population(IDx==k); 11 | end 12 | end 13 | -------------------------------------------------------------------------------- /On-GMMOR/GMM_cluster.m: -------------------------------------------------------------------------------- 1 | function [IDx,Sigma]=GMM_cluster(X,Kmax) 2 | warning off 3 | %% input: X is the data set, each row is a sample 4 | %% input: Kmax is the maximum number of cluster 5 | %% output: IDx is the cluster ID of each solution 6 | %% output: Sigma is the covariance matrix of each cluster 7 | 8 | Options=statset('TolFun',10e-08,'MaxIter',200); 9 | for k=1:Kmax 10 | gmfit{k} = fitgmdist(X,k,'RegularizationValue',0.001, 'CovarianceType','diagonal','Replicates',20,'Start','randSample','Options',Options); 11 | %gmfit{k} = fitgmdist(X,k,'RegularizationValue',10e-04, 'CovarianceType','full','Replicates',20,'Start','randSample','Options',Options); 12 | bic(k)=gmfit{k}.BIC; 13 | SIGMA{k}=gmfit{k}.Sigma; 14 | end 15 | bic_k=[bic',[1:Kmax]']; 16 | min_bic_k=min(bic_k); 17 | max_bic_k=max(bic_k); 18 | norm_bic_k=(bic_k-repmat(min_bic_k,Kmax,1))./repmat(max_bic_k-min_bic_k,Kmax,1); 19 | d=sqrt(sum(norm_bic_k.^2,2)); 20 | [~,k]=min(d); 21 | IDx=cluster(gmfit{k},X); 22 | Sigma=SIGMA{k}; 23 | end -------------------------------------------------------------------------------- /On-GMMOR/README.md: -------------------------------------------------------------------------------- 1 | Li G, Wang Z, Zhang Q, et al. Offline and online objective reduction via Gaussian mixture model clustering[J]. IEEE Transactions on Evolutionary Computation, 2022, 27(2): 341-354. 2 | 3 | This code is implemented in the Platform PlatEMO-v2.9. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2023 CIAM Group 2 | **The code can only be used for non-commercial purposes. Please contact the authors if you want to use this code for other purposes.** 3 | **All publications using this code should properly cite the corresponding paper.
** 4 | **** 5 | The codes of evolutionary algorithms for single/multi-objective optimization problems. 6 | -------------------------------------------------------------------------------- /SAMFEO_Code/RBFInterp.m: -------------------------------------------------------------------------------- 1 | function y = RBFInterp(x, para) 2 | ax = para.nodes; 3 | nx = size(x, 1); 4 | np = size(ax, 1); % np: the size of data set 5 | 6 | % xmin = para.xmin; 7 | % xmax = para.xmax; 8 | % ymin = para.ymin; 9 | % ymax = para.ymax; 10 | % % normalization 11 | % % x = 2./(repmat(xmax - xmin, nx, 1)) .* (x - repmat(xmin, nx, 1)) - 1; 12 | % for i = 1 : length(xmin) 13 | % if xmin(i) ~= xmax(i) 14 | % x(:, i) = 2 ./ (repmat(xmax(i) - xmin(i), nx, 1)) .* (x(:, i) - repmat(xmin(i), nx, 1)) - 1; 15 | % end 16 | % end 17 | 18 | r = dist(x, ax'); 19 | switch para.kernel 20 | case 'gaussian' 21 | Phi = radbas(sqrt(-log(.5))*r); 22 | case 'cubic' 23 | Phi = r.^3; 24 | case 'multiquadric' 25 | Phi=sqrt(r.^2+0.8^2); 26 | end 27 | 28 | y = Phi * para.alpha + [ones(nx, 1), x] * para.beta; 29 | % renormalization 30 | % y = repmat(ymax - ymin, nx, 1)./2 .* (y + 1) + repmat(ymin, nx, 1); 31 | % for i = 1 : length(ymin) 32 | % if ymin(i) ~= ymax(i) 33 | % y(:, i) = repmat(ymax(i) - ymin(i), nx, 1)./2 .* (y(:, i) + 1) + repmat(ymin(i), nx, 1); 34 | % end 35 | % end 36 | end -------------------------------------------------------------------------------- /SAMFEO_Code/Repair.m: -------------------------------------------------------------------------------- 1 | %% repair the solution that violates the boundary constraints 2 | function x = Repair(x, LU) 3 | xL = LU(1,:); 4 | xU = LU(2,:); 5 | for i = 1 : size(x, 1) 6 | indexLower = find(x(i, :) < xL); 7 | indexUper = find(x(i, :) > xU); 8 | x(i, indexLower) = min(xU(indexLower), 2 * xL(indexLower) - x(i, indexLower)); 9 | x(i, indexUper) = max(xL(indexUper), 2 * xU(indexUper) - x(i, indexUper)); 10 | % x(i, indexLower) = xL(indexLower); 11 | % x(i, indexUper) = xU(indexUper); 12 | end 13 | end -------------------------------------------------------------------------------- /SAMFEO_Code/cec17_func.cpp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/SAMFEO_Code/cec17_func.cpp -------------------------------------------------------------------------------- /SAMFEO_Code/cec17_func.mexw32: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/SAMFEO_Code/cec17_func.mexw32 -------------------------------------------------------------------------------- /SAMFEO_Code/cec17_func.mexw64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/SAMFEO_Code/cec17_func.mexw64 -------------------------------------------------------------------------------- /SAMFEO_Code/input_data.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CIAM-Group/EvolutionaryAlgorithm_Codes/cd1f828e4ccfa99260d1da5624bdca81af66e316/SAMFEO_Code/input_data.zip -------------------------------------------------------------------------------- /SAMFEO_Code/randFCR.m: -------------------------------------------------------------------------------- 1 | function [F,CR] = randFCR(NP, CRm, CRsigma, Fm, Fsigma) 2 | 3 | % this function generate CR according to a normal distribution with mean "CRm" and sigma "CRsigma" 4 | % If CR > 1, set CR = 1. If CR < 0, set CR = 0. 5 | % this function generate F according to a cauchy distribution with location parameter "Fm" and scale parameter "Fsigma" 6 | % If F > 1, set F = 1. If F <= 0, regenrate F. 7 | % 8 | % Version: 1.1 Date: 11/20/2007 9 | % Written by Jingqiao Zhang (jingqiao@gmail.com) 10 | 11 | %% generate CR 12 | CR = CRm + CRsigma * randn(NP, 1); 13 | CR = min(1, max(0, CR)); % truncated to [0 1] 14 | 15 | %% generate F 16 | F = randCauchy(NP, 1, Fm, Fsigma); 17 | F = min(1, F); % truncation 18 | 19 | % we don't want F = 0. So, if F<=0, we regenerate F (instead of trucating it to 0) 20 | pos = find(F <= -1); 21 | while ~ isempty(pos) 22 | F(pos) = randCauchy(length(pos), 1, Fm, Fsigma); 23 | F = min(1, F); % truncation 24 | pos = find(F <= -1); 25 | end 26 | % Cauchy distribution: cauchypdf = @(x, mu, delta) 1/pi*delta./((x-mu).^2+delta^2) 27 | function result = randCauchy(m, n, mu, delta) 28 | % http://en.wikipedia.org/wiki/Cauchy_distribution 29 | result = mu + delta * tan(pi * (rand(m, n) - 0.5)); 30 | --------------------------------------------------------------------------------