├── .ipynb_checkpoints
└── Kepler_Jason-checkpoint.ipynb
├── Index-Tracking-Portfolio-Optimization.html
├── Index-Tracking-Portfolio-Optimization.ipynb
├── jupyter nbconvert --to html --template toggle Index-Tracking-Portfolio-Optimization.ipynb.txt
├── report.log
├── requirements.txt
└── toggle.tpl
/jupyter nbconvert --to html --template toggle Index-Tracking-Portfolio-Optimization.ipynb.txt:
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https://raw.githubusercontent.com/jasonyip184/Index-Tracking-Portfolio-Optimization/f62d42a67a89af0dc839fa19865b3cb08104130b/jupyter nbconvert --to html --template toggle Index-Tracking-Portfolio-Optimization.ipynb.txt
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/report.log:
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1 | 2020-04-21 19:43:42,422 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
2 | 2020-04-21 19:43:42,572 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 3.1236762337268866e-07, best pos: [ 0.00018253 -0.00052825]
3 | 2020-04-21 19:44:36,008 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
4 | 2020-04-21 19:44:36,138 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 1.0918111941226326e-06, best pos: [ 0.00033718 -0.000989 ]
5 | 2020-04-21 19:56:08,028 - pyswarms.discrete.binary - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
6 | 2020-04-21 19:58:55,376 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.19279999999999997, best pos: [1 1 1 1 1 1 1 1 1 1 0 1 1 1 1]
7 | 2020-04-21 20:07:03,652 - pyswarms.discrete.binary - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
8 | 2020-04-21 20:09:47,530 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.19279999999999997, best pos: [1 1 1 1 1 1 1 1 1 1 0 1 1 1 1]
9 | 2020-04-21 20:16:33,726 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
10 | 2020-04-21 20:16:35,570 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 2.0932535492408223e-22, best pos: [1. 1.]
11 | 2020-04-21 20:19:54,779 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.6, 'c2': 0.3, 'w': 0.4}
12 | 2020-04-21 20:19:54,911 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.005413289522101415, best pos: [0.04548598 0.05783005]
13 | 2020-04-21 20:20:52,064 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.6, 'c2': 0.3, 'w': 0.4}
14 | 2020-04-21 20:20:52,217 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.4918240510059904, best pos: [0.12448418 0.01298071 0.26949499 0.32104009 0.3410557 0.11653514
15 | 0.03814328 0.09733449 0.3284752 0.22746571]
16 | 2020-04-21 20:22:04,946 - pyswarms.discrete.binary - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
17 | 2020-04-21 20:22:17,922 - pyswarms.discrete.binary - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
18 | 2020-04-21 20:22:19,372 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.21839999999999998, best pos: [1 1 1 1 0 1 1 0 1 1 1 1 1 1 1]
19 | 2020-04-21 20:22:19,422 - pyswarms.discrete.binary - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
20 | 2020-04-21 20:22:20,988 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.2264, best pos: [1 1 1 0 1 1 0 1 1 1 1 1 1 1 0]
21 | 2020-04-21 20:22:53,598 - pyswarms.discrete.binary - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
22 | 2020-04-21 20:22:55,406 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.20879999999999996, best pos: [1 1 1 0 1 1 0 1 1 1 0 1 1 1 1]
23 | 2020-04-21 20:23:01,288 - pyswarms.discrete.binary - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
24 | 2020-04-21 20:23:03,012 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.20879999999999996, best pos: [1 1 0 1 1 1 1 1 1 0 0 1 1 1 1]
25 | 2020-04-21 20:23:23,261 - pyswarms.discrete.binary - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
26 | 2020-04-21 20:23:32,306 - pyswarms.discrete.binary - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 5, 'p': 2}
27 | 2020-04-21 20:23:32,663 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.2344, best pos: [1 1 1 0 1 1 1 1 1 1 0 0 1 1 0]
28 | 2020-04-21 20:23:41,032 - pyswarms.discrete.binary - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 5, 'p': 2}
29 | 2020-04-21 20:23:41,375 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.20879999999999996, best pos: [1 1 0 0 1 1 1 1 1 1 0 1 1 1 1]
30 | 2020-04-21 20:24:20,620 - pyswarms.discrete.binary - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
31 | 2020-04-21 20:24:22,306 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.20079999999999995, best pos: [1 1 1 1 1 1 0 1 1 1 0 1 1 1 1]
32 | 2020-04-21 20:24:30,956 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
33 | 2020-04-21 20:24:31,420 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.2344, best pos: [1 0 1 1 0 1 1 0 1 1 1 1 0 1 1]
34 | 2020-04-21 21:34:49,536 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
35 | 2020-04-21 21:34:49,890 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.26, best pos: [1 1 0 1 0 0 1 0 1 0 1 1 1 1 1]
36 | 2020-04-21 21:35:10,836 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
37 | 2020-04-21 21:35:11,405 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.21679999999999996, best pos: [1 1 0 0 1 1 0 1 1 1 0 1 1 1 1]
38 | 2020-04-21 21:35:29,953 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
39 | 2020-04-21 21:35:30,491 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.2424, best pos: [1 1 0 1 1 1 0 1 1 0 1 0 1 1 0]
40 | 2020-04-21 21:35:57,632 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
41 | 2020-04-21 21:35:58,879 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.19279999999999997, best pos: [1 1 1 1 1 1 1 1 1 1 0 1 1 1 1]
42 | 2020-04-21 21:37:00,803 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
43 | 2020-04-21 21:37:01,335 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.252, best pos: [1 0 0 0 1 1 1 1 1 1 1 0 1 1 1]
44 | 2020-04-21 21:38:22,320 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
45 | 2020-04-21 21:38:22,839 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.2592, best pos: [1 1 1 1 0 0 1 1 1 0 0 0 0 1 1]
46 | 2020-04-21 21:38:37,803 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
47 | 2020-04-21 21:38:38,338 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.21839999999999998, best pos: [1 1 1 1 1 1 1 1 1 0 1 1 0 1 1]
48 | 2020-04-21 21:38:58,078 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
49 | 2020-04-21 21:38:58,668 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 0.20079999999999995, best pos: [1 1 1 1 1 0 1 1 1 1 0 1 1 1 1]
50 | 2020-04-21 21:40:34,683 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
51 | 2020-04-21 21:40:41,932 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
52 | 2020-04-21 21:40:41,958 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 1.0, best pos: [1 0 1 0 1 1 0 0 1 0 0 1 0 0 0]
53 | 2020-04-21 21:41:43,142 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
54 | 2020-04-21 21:41:43,175 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 1.0, best pos: [1 0 1 0 1 1 0 0 0 0 1 1 1 0 0]
55 | 2020-04-21 21:41:48,536 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
56 | 2020-04-21 21:41:48,550 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 1.0, best pos: [0 0 1 0 0 0 1 0 0 1 1 1 1 1 0]
57 | 2020-04-21 21:42:02,697 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
58 | 2020-04-21 21:42:02,710 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 1.0, best pos: [0 0 1 0 1 1 1 1 1 1 1 1 0 0 0]
59 | 2020-04-21 21:42:33,657 - pyswarms.discrete.binary - INFO - Optimize for 3 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9, 'k': 30, 'p': 2}
60 | 2020-04-21 21:42:33,682 - pyswarms.discrete.binary - INFO - Optimization finished | best cost: 1.0, best pos: [1 0 0 1 1 1 0 1 1 1 1 0 1 1 0]
61 | 2020-04-21 21:48:25,567 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
62 | 2020-04-21 21:48:27,133 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 1.0, best pos: [0.6007836 0.73868104]
63 | 2020-04-21 21:48:38,491 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
64 | 2020-04-21 21:48:38,613 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 1.0, best pos: [0.9433023 0.3153813]
65 | 2020-04-21 21:48:46,921 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3}
66 | 2020-04-21 21:48:46,925 - pyswarms.backend.operators - ERROR - Missing keyword in swarm.options
67 | Traceback (most recent call last):
68 | File "C:\Users\jasonyip184\anaconda3\envs\kepler\lib\site-packages\pyswarms\backend\operators.py", line 130, in compute_velocity
69 | w = swarm.options["w"]
70 | KeyError: 'w'
71 | 2020-04-21 21:48:56,782 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'w': 0.9}
72 | 2020-04-21 21:48:56,786 - pyswarms.backend.operators - ERROR - Missing keyword in swarm.options
73 | Traceback (most recent call last):
74 | File "C:\Users\jasonyip184\anaconda3\envs\kepler\lib\site-packages\pyswarms\backend\operators.py", line 129, in compute_velocity
75 | c2 = swarm.options["c2"]
76 | KeyError: 'c2'
77 | 2020-04-21 21:48:58,607 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
78 | 2020-04-21 21:48:58,729 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 1.0, best pos: [0.95771262 0.11146389]
79 | 2020-04-21 21:49:04,382 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
80 | 2020-04-21 21:49:04,505 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 1.0, best pos: [0.39138936 0.82073131 0.21806712 0.40545435 0.82438691]
81 | 2020-04-21 21:51:29,434 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
82 | 2020-04-21 21:52:06,577 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
83 | 2020-04-21 21:52:06,794 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.6184475987227374, best pos: [ 0.09926964 0.45941394 -0.13110902 0.68146895 -0.62570227 0.19016656
84 | 0.30932613 -0.43166707 0.40277917 -0.14652374 -0.08762962 0.44064707
85 | 0.43309492 0.71555102 -0.04220844 -0.2074954 -0.02849613 -0.28618042
86 | -0.25918445 0.28516536 0.66717673 0.09460681 -0.19593609 0.67219497
87 | 1.37726397 0.04958704 0.4331129 0.93472377 1.2538921 0.1219892
88 | 0.43570883 0.18671292 0.74948966 0.06483666 0.94299218 0.01115412
89 | 0.487886 0.46122036 -0.0609525 0.70524066 -0.08357054 0.87254522
90 | 0.53783443 0.88674792 -0.41043247 0.9671708 0.32054743 -0.15359923
91 | 0.62063089 -0.15469891 0.35390658 0.16118769 1.35036064 -0.23813104
92 | 0.58882924 0.46364822 0.66895285 0.07499283 0.32103913 0.54607748
93 | 0.30975904 -0.26215506 0.15820949 0.15382257 0.16564183 0.24685862
94 | 0.343772 0.51347532 0.58853919 -0.05973399 -0.02597609 -0.83923411
95 | 0.29393393 -0.74376247 -0.4790189 0.26878296 0.12978263 0.47537206
96 | 0.2440976 0.28478347 0.59872989 0.31945046 -0.23142318 0.25421847
97 | 0.26671824 0.90135845 0.25897579 0.67261898 0.60086235 0.68181159
98 | 0.52183634 -0.06726839 0.0925893 0.9161775 0.74453098 0.23050793
99 | 0.28626498 -0.18401055 0.36259046 0.57905164 0.73261311 -0.19660696
100 | 0.99286823 0.43889633 0.29970577 -0.45665313 -0.11601388 0.1621277
101 | 0.7199335 -0.10207504 0.70803456 0.87868543 1.09894384 -1.00202999
102 | 1.01069765 0.64424084 0.24928025 -0.32059468 0.76565388 0.31703507
103 | -0.41481516 -0.15768325 0.24219048 -0.34665518 0.22777718 -0.38064757
104 | 0.48302885 -0.32347601 0.38501179 -1.2893412 1.02404515 0.49985156
105 | 0.36287079 -0.03733882 -0.20215506 0.3230951 -0.01604193 0.77384691
106 | 0.3075563 -0.51618031 0.05172436 0.11067971 0.46307387 0.20069678
107 | -0.26667624 -0.06704495 0.64426608 0.78024876 0.24637818 -1.27287541
108 | 0.008321 -0.16831682 -0.22999306 0.24563369 0.50907415 0.02590645
109 | 0.40193943 -0.10084875 0.19101249 0.59021722 0.0818898 0.98735631
110 | -0.30032259]
111 | 2020-04-21 21:52:16,546 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
112 | 2020-04-21 21:52:16,768 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.6847200653353049, best pos: [ 0.10503156 0.54457261 0.15536659 -0.30181631 0.45632487 -0.59494654
113 | -0.16688793 0.00905961 0.05999781 -0.20524718 0.44540058 -0.06132511
114 | 0.31865891 -0.26190084 0.88899562 0.26206545 0.40763949 -0.57983725
115 | 0.62956779 0.65531181 0.10255978 -0.00933183 -0.20598527 -0.60073345
116 | -0.15517935 0.05787475 -0.44674159 0.77564634 0.17514431 0.24557117
117 | -0.70913702 -0.27877088 0.03765779 0.24622388 -0.37690306 0.33101548
118 | 0.14728848 0.14883001 -1.34151427 -0.37452625 -0.39462799 0.7346449
119 | -0.02568022 -0.01554634 0.69243112 -0.53063711 0.69222645 -0.13308471
120 | -0.47050283 0.27335125 0.67402894 0.16626773 0.52947954 0.19728932
121 | -0.58393606 0.64064332 0.31227104 0.48302706 -0.34561384 0.76241334
122 | -0.31151088 -0.13231686 -0.31763995 0.14072254 0.40165501 0.06763792
123 | -0.33400228 -0.45198537 -0.25699199 0.07809054 -0.56522766 0.06635729
124 | 0.72494411 0.20889311 0.25578122 0.40214529 0.42864455 0.47142943
125 | 0.40610125 0.28213147 0.12538918 0.10576811 0.86470654 -0.58188451
126 | -0.16011147 0.00933371 0.83892436 -0.11821173 0.19799785 -0.20252542
127 | -0.08392746 1.02848542 0.67868742 -0.77268885 0.39988896 0.7942391
128 | -0.01061076 0.142937 0.35413191 -0.21955513 -0.241291 0.50063207
129 | -0.64606432 0.71900888 0.06085415 0.19872369 0.43641602 0.4392472
130 | -0.68334598 0.41256224 0.1323749 0.1083897 -0.3014467 0.15067501
131 | -0.11271913 -0.22264386 0.21694553 0.16922414 0.33387382 0.12645941
132 | 0.15006824 -0.37642892 0.58643336 -0.00977012 0.33845024 -0.68951538
133 | 0.06900447 -0.3011236 0.16478032 0.50627732 0.50943572 0.46009486
134 | -0.28772113 -0.3420059 0.46091853 0.14051402 0.37220196 -0.2858806
135 | 0.08774097 0.17809395 -0.17944599 0.11406531 -0.13766504 0.04436041
136 | -0.4118014 -0.24540064 0.21284498 0.08522692 0.13973308 0.27087698
137 | 0.82468862 -0.76650253 -0.55347162 0.74786179 -0.30887244 0.19509878
138 | 0.07844533 0.52691634 -0.68440879 -0.10804929 -0.72294524 -0.34781781
139 | 0.37611295]
140 | 2020-04-21 21:52:26,937 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
141 | 2020-04-21 21:52:27,156 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.6840343416372849, best pos: [ 0.53547241 -0.17876036 -0.10654647 -0.05940677 -0.16553896 -1.04859164
142 | -0.33996044 0.25518568 0.24257496 -0.5751901 -0.27956966 -0.24606004
143 | 0.14158661 0.38860239 0.21261374 0.17786371 0.60772843 -0.29878668
144 | -0.26810818 -0.19233136 -0.26995084 0.13782544 0.38973585 0.73003433
145 | -0.87709635 -0.37198263 -0.19935897 -0.00207694 0.55495634 0.1458495
146 | -0.07533832 -0.72368743 -0.656967 0.00464736 -0.13719328 0.2142644
147 | -0.72300489 0.51420886 0.25549652 -0.20715313 -0.16822256 0.08459856
148 | -0.88669937 -0.40989874 0.07290836 0.03490885 0.06133999 -0.11483334
149 | -0.14828091 -0.01071746 0.83597424 0.33417055 -0.23963118 -0.16302134
150 | 0.5779591 -0.34643749 -0.57150984 -0.12611888 -1.44840608 0.16292435
151 | 0.59776826 -0.99835388 -1.20961767 0.16601831 0.03198362 -0.02661292
152 | -0.64735657 0.3149684 -0.34570984 -0.6374636 -0.55265096 -0.30698272
153 | 0.27889295 0.09740744 0.44565193 0.11652583 -0.21993355 -0.0802984
154 | -0.13177063 0.09914589 -0.07695643 0.4946359 0.08382518 -0.7202309
155 | -0.21269908 -0.50591087 -0.75404815 0.19176115 -0.6533752 -0.69584298
156 | -0.14373557 -0.0215511 0.03336537 0.28598479 0.01210187 0.03971204
157 | -0.29980539 -0.03520647 0.18587721 0.41387654 -0.47055585 0.2251937
158 | -0.81079467 0.27082715 -0.31688132 0.01752684 -0.68437298 -0.17098578
159 | -0.09314666 0.34747809 -1.14660215 -0.54061146 -0.10684259 0.56516322
160 | -0.23040459 -0.34968822 -0.30379158 -0.33414448 0.00239767 -0.18777798
161 | 0.00338645 -0.18879596 0.17287965 0.60856497 0.17870788 0.21438524
162 | -0.03957219 -0.35119489 0.90992924 -0.09034628 -1.28847641 -0.27426076
163 | 0.26741629 -0.13490374 -0.53524099 -0.57834637 0.52145186 0.16922661
164 | 0.48730757 -0.06605695 -0.54794582 0.66540415 0.15081084 -0.30823886
165 | -0.3165143 -0.07669557 -0.6476229 -0.27024366 -0.16775039 -1.07279667
166 | -0.4596923 0.06059988 0.33820699 0.20161606 0.64647063 -0.15117676
167 | -0.34568542 -0.63090324 0.05125355 -0.01637595 0.08504824 -0.54023094
168 | -0.99050342]
169 | 2020-04-21 21:52:41,141 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
170 | 2020-04-21 21:52:41,359 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.7015418160493865, best pos: [-0.1971698 -0.25060082 0.40202224 0.1896216 -0.00341151 -0.51699421
171 | 0.28342486 -0.29338411 -0.35048825 0.11240106 0.68505362 0.03636099
172 | 0.23123006 0.34335393 -0.02397961 0.01733607 -0.1656461 -0.33928301
173 | -0.27407762 -0.01655324 0.26780551 0.06530921 -0.23049927 -0.26960501
174 | 0.16150523 -0.04720161 0.14406791 0.36429102 0.07963777 -0.18701403
175 | 0.3505956 0.75051634 0.49595322 -0.18083989 0.22162509 -0.92579773
176 | -0.07876289 0.14806879 0.21785194 -0.06039176 -0.16878701 0.2540203
177 | -0.40511341 0.1765029 -0.78458761 -0.09850408 -0.50298776 0.64646034
178 | 0.16667195 0.17566981 0.45008946 0.43637237 -0.69859966 0.4872517
179 | -0.16134596 -0.19398867 0.34147946 -0.25820767 0.32184025 0.58904781
180 | -0.64854191 0.03702201 -0.17683845 0.34985015 0.19747128 0.05890718
181 | 0.21249491 0.25391266 0.58123486 0.58030539 0.1247346 0.2441437
182 | 0.70757371 -0.37071602 0.68212528 0.7740831 0.17625133 0.15571706
183 | -0.51066104 0.6424913 0.25905643 -0.11237057 0.02559449 0.00525325
184 | 0.2409498 0.23910114 -0.35377332 -0.60996305 0.14396911 0.5327413
185 | -0.1199524 0.03363996 0.01982565 0.03805999 -0.76950665 0.60275214
186 | 0.72540392 -0.40739523 0.29549633 0.66710116 0.82415486 -0.35561358
187 | 0.42957236 -0.04770903 -0.42323735 0.67278471 0.04805176 -0.94362024
188 | -0.56795991 -0.29612475 0.33885988 0.2108953 -0.21440911 -0.87645038
189 | 0.28769808 0.09320985 -0.48096798 -0.07780274 0.48494669 -1.10107497
190 | 0.54909411 -0.39044633 -0.05149226 0.22780678 -0.17994936 0.01294012
191 | 0.3896125 -0.2903995 -0.25948841 0.75376139 0.29697499 -0.06693484
192 | 0.67042546 -0.48795182 0.4649352 0.09497137 0.55215627 0.71909211
193 | -0.71442594 0.33939544 -0.28012676 0.55291689 0.01677413 0.14336191
194 | -0.06687494 -0.30226888 0.14542058 -0.12126929 -0.05917998 0.2744257
195 | 0.5816788 0.25976625 0.2214929 0.14934248 -0.60635766 0.04579338
196 | -0.32850011 0.24549991 -0.61069946 -0.27142902 0.17916662 -0.36895033
197 | 0.26208368]
198 | 2020-04-21 21:52:45,528 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
199 | 2020-04-21 21:52:45,739 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.7677846465846913, best pos: [ 0.03851692 0.31370654 1.23600853 -0.09212152 0.39101144 0.04343654
200 | -0.60585158 0.63614979 0.2299344 0.16054152 0.13202424 0.20768269
201 | -0.03697039 0.10745908 -0.42614247 -0.72836301 -0.40612887 -0.03683783
202 | 0.77254929 -0.6497688 -0.13138106 0.25186299 0.61754638 0.56800183
203 | -0.40760058 1.04406584 0.81732091 0.00706129 -0.35559049 0.21252761
204 | 0.35899219 -0.25222801 -0.32445148 0.49177065 0.08700309 -0.26034805
205 | -0.56330308 -0.30560004 0.08434628 -0.07554104 0.25430158 -0.37692086
206 | 0.54600475 -0.19766032 -0.32825843 -0.72143512 -0.83544027 -0.03293815
207 | 0.66595734 -0.33701707 -0.16578744 -0.95379904 -1.12876457 0.21492631
208 | -0.66041146 -0.23634978 -0.00788392 -1.08257745 -1.29712546 0.24978707
209 | 0.0701083 -0.01798502 -0.30511736 -0.30907326 0.02506562 -0.26610757
210 | 0.19060879 0.4068951 0.58821631 0.33596593 1.10840018 -0.30945692
211 | 0.76744053 0.21523804 0.11472425 0.18910026 0.56089038 -0.1849238
212 | -1.79517413 0.28374205 0.04544013 -0.94664451 -0.271486 0.34342876
213 | 0.05216376 0.73650963 0.65293234 -0.29966668 0.30463144 -0.60404946
214 | 0.26735056 0.96643751 -0.25280044 -0.30808298 0.76845573 0.40307859
215 | -0.49604594 0.25721827 0.02826505 0.07245351 0.41836976 0.19608352
216 | -0.18930592 0.85494262 0.30719021 -0.63345922 0.21264042 0.02443193
217 | -0.09224933 0.60555511 0.60767475 -0.07653223 0.52621698 -0.20566956
218 | 1.24605771 0.49726016 -0.10060329 -0.08953925 0.76380264 -0.1770651
219 | -1.11721298 -0.06360077 0.19888565 0.66416644 -0.18278774 -0.6467705
220 | -0.14466537 0.49595202 0.42184166 1.5212384 0.19438434 0.38574003
221 | -0.92411528 -0.01066169 0.06737563 0.55767192 0.32421876 0.28658105
222 | 0.34953459 0.09625226 0.54076437 0.16318353 -0.42205407 -0.07413018
223 | 0.46687425 0.29329518 0.53433236 0.57277517 -0.49103897 0.80548042
224 | 0.22508045 0.36743057 0.01503355 -0.08457122 0.39964191 -0.57416681
225 | -0.04939435 0.33659582 -0.38096851 0.01540112 -0.07108881 -0.50144292
226 | 0.34458645]
227 | 2020-04-21 21:52:53,873 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
228 | 2020-04-21 21:52:54,101 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.7161628380350002, best pos: [-0.65970426 -0.18886006 -0.28413733 0.03216565 0.27504721 -0.14090239
229 | 0.08316016 0.09381329 -0.03785043 -0.30550364 -0.04101136 0.05628041
230 | 0.27424402 -0.63891552 0.22626312 0.63282814 0.09785201 -0.37080948
231 | -0.25629016 -0.77201999 -1.20610528 -0.11719461 0.49280064 -0.33726448
232 | -0.52366953 0.50839141 -0.01330573 -0.08543719 0.04680216 0.02085586
233 | 0.14278311 -0.9958362 -0.19374545 0.61075644 -0.12486909 0.04139134
234 | 0.14008389 0.76706966 0.16916832 0.56902948 -0.07911517 0.47700666
235 | -0.55965987 -0.61696248 -0.78116175 0.38138559 -0.15886067 0.21972905
236 | -0.37437048 0.21861798 0.3309033 -0.48828912 -0.43107292 -0.97564897
237 | 0.48606095 0.26883021 -0.18415327 0.17091734 -0.89607104 -0.01608716
238 | -0.52839281 -0.90159663 0.04953337 -0.53049539 -0.01225122 0.26938987
239 | -0.56622057 -0.6737796 -0.36146421 -0.30373875 0.51921219 -0.44229036
240 | -0.25577987 -0.22266618 0.03641065 -0.19988547 -0.54987749 -0.03709228
241 | -0.59000715 0.14879848 0.83357115 -0.01843453 0.66434476 -0.5683036
242 | -0.51892934 -0.53219015 -0.76892272 -0.69353767 0.23573916 0.13169594
243 | -0.50578525 -0.3028913 0.23274105 -0.8474932 0.07002773 0.59019541
244 | 0.32971899 -0.19703853 0.32970579 0.05907894 -0.43211092 -0.30227862
245 | -0.20366283 -0.61266147 0.1818597 0.70390901 0.356589 -0.1216065
246 | -0.69660471 -0.05044992 -0.56765695 -0.22129374 -0.95232283 -0.26215874
247 | -0.41778443 0.16826586 -0.31108404 -0.22457131 -0.53202443 -0.70988061
248 | 0.03814381 0.49875642 0.26947511 -0.54525994 0.03124134 -0.17464329
249 | -0.73611927 0.61313364 0.33771823 -0.17066007 -0.33026875 -0.53142016
250 | 0.36803277 0.65804927 -0.01349597 0.25420285 0.28403031 -0.5271904
251 | -0.55857478 -0.3831898 0.2325369 0.25494121 0.45574446 0.27212257
252 | -0.33397654 -0.40574678 0.29825601 0.74095088 0.23076965 -1.20052013
253 | -0.33014312 0.08531626 -0.6591986 0.22106881 -0.60173232 -0.76542078
254 | 0.53419062 0.42069612 0.24996794 0.14057426 -0.47510139 -0.15731816
255 | -0.74795416]
256 | 2020-04-21 21:54:32,885 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
257 | 2020-04-21 21:54:33,093 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.7079563860413541, best pos: [-0.41237209 0.21480177 0.61898484 1.13013833 -0.62347538 -0.81323018
258 | 0.2889736 0.36522811 0.8862557 0.12790817 -0.31249089 0.89276193
259 | 0.06595025 -0.19267767 0.60338268 0.90616787 0.02781054 -0.27314984
260 | -0.38696506 -0.21805916 0.46564431 0.11531271 -0.07546844 0.16666718
261 | 0.34251626 0.73069159 0.90769658 -0.30668211 0.47962995 0.15590613
262 | -0.10381486 -0.4816468 0.44078507 1.21266436 -0.09281639 -0.39399594
263 | -0.18914516 0.15732489 0.29920463 0.62356812 0.50482852 0.27509965
264 | 0.05561643 0.76231215 -0.49948704 0.74712136 -0.22524863 0.437817
265 | 0.74145751 -0.16308559 0.30977512 0.44172209 1.08713776 -0.53306078
266 | 0.0240958 -0.07543141 0.00185844 -0.43476798 1.07605679 0.95062946
267 | 0.2970637 0.28703582 -1.32008871 0.16350805 -0.72094989 0.75591925
268 | 0.4934104 0.86861632 -0.53480967 0.33784037 0.55155428 0.35247882
269 | 0.76552739 0.91357891 0.54183462 -0.43248603 0.15261066 -0.90961657
270 | 0.95008821 1.25147849 0.25613605 -0.62206238 -0.356799 0.27419762
271 | 0.25441958 -0.32409366 -0.16732297 -0.47259244 -1.43217578 0.87116721
272 | 0.752664 0.44946741 -0.30163073 0.46595438 1.45925452 0.54709619
273 | 0.93744482 0.2888907 0.62014304 0.13108559 0.86361885 -0.65023825
274 | 0.26212656 0.11724336 0.5100039 0.47497558 0.24602632 1.33840464
275 | 0.39357794 0.62128218 0.03390159 0.94910413 0.96692874 -0.60241847
276 | 0.72584859 -0.64093847 0.51441395 0.79373458 0.12379059 -0.63063713
277 | 0.35809835 -0.39294559 0.12353444 -0.3478536 -0.35033626 0.79301383
278 | 0.18487957 0.72805306 -0.5695566 0.73814795 0.48624286 1.411767
279 | 1.67938392 0.84002896 1.05990087 0.76414428 0.24797302 1.0895824
280 | -0.21583785 0.84030726 -0.24630835 0.48224483 0.61792005 0.33718169
281 | -1.09112746 0.40736288 0.20928122 0.70607118 0.36352939 0.16727294
282 | 1.27458598 -0.58360503 -1.47992135 0.26417518 -0.30113745 0.33042321
283 | 0.00506699 -0.29588408 0.03841845 0.36221912 0.55880007 -0.67949329
284 | 0.887155 ]
285 | 2020-04-21 21:54:50,374 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
286 | 2020-04-21 21:54:50,647 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.6719928594220524, best pos: [-0.05388412 -0.00328297 -1.05419819 0.04118226 -0.16080572 -0.27092815
287 | -0.87597517 -0.79521116 -0.28721226 -0.43843116 -0.71799223 -0.32986609
288 | -1.58555387 -0.97750126 -1.68066125 -0.42815509 -0.63053222 -1.48944286
289 | -0.05618376 -1.7550699 -0.04182167 0.54495893 -0.57325189 -0.03695411
290 | -0.58408082 0.0770184 -1.57129173 -0.51289535 -0.39553724 -0.02428887
291 | -0.10348504 -0.34656529 -0.7030939 0.32083872 -0.14365683 0.40117805
292 | 0.13304975 -0.32675141 -0.6802184 -0.38485044 -1.67898821 -1.19151973
293 | -0.10584042 -0.5743308 -0.11674818 0.03952421 -0.06039641 -1.15218707
294 | -0.26043939 -0.08514455 0.18128267 -2.37791305 0.0329213 -0.44804282
295 | -0.14331447 0.70305213 -1.21579691 -1.10738023 -2.62848506 0.03595891
296 | -0.84577019 -0.99102183 -0.52285708 -0.8350312 -2.30850628 -1.87687616
297 | 0.43051336 0.40520571 -1.02259357 0.26990106 -0.32025768 -1.58116841
298 | -0.78969667 -0.36567479 -0.26491519 0.01734968 -0.49879536 -0.82980771
299 | 0.41966422 -0.91663959 0.09716287 -1.33611762 0.36378913 -0.56967339
300 | -1.48188553 0.22390233 -0.61926746 0.02622803 -0.53144938 -1.89598089
301 | -1.1064116 -1.243785 -0.51854568 -0.24604767 -0.47556402 -0.81120712
302 | -1.98330034 -0.97336649 0.18780213 -0.88727606 -0.23860891 -0.17205845
303 | -0.8912441 -0.33181003 -1.70369633 0.49903905 -1.90750955 0.32641458
304 | -0.24215996 0.31738841 -0.31414531 -0.89763539 -0.08464561 -0.33086908
305 | 0.21284334 0.07449375 -0.51148309 -0.75929154 -0.79714163 -1.06423972
306 | 0.16521561 -1.00912791 0.12758172 -0.53288038 0.57058866 -0.07892598
307 | -0.61663774 -0.92643323 -0.05514446 0.37823143 -1.75724122 -0.24472713
308 | -1.31531127 -0.59440669 0.08526762 -0.8863315 0.02951355 0.32528095
309 | 0.24051383 -1.08055502 -1.00730128 -0.40610583 -1.00170402 -0.87238313
310 | -1.39511439 -2.85667228 -0.02004084 0.80579482 -0.85056502 -1.2069592
311 | -0.0732998 -1.29016547 -0.47079339 0.01671139 -0.61777975 -1.67690118
312 | -0.03920709 0.3040939 -0.49145999 -1.65702933 -0.47849543 -0.17842015
313 | -0.08199831]
314 | 2020-04-21 21:55:33,082 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
315 | 2020-04-21 21:55:33,302 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.8779728279251674, best pos: [ 1.81710346e-01 -5.73124544e-01 1.18080879e+00 2.16993353e-01
316 | 1.87737666e-01 1.53650489e-01 -3.00347619e-01 -1.02263645e-01
317 | -1.31918001e-01 6.64065725e-01 4.13360931e-01 9.63830700e-02
318 | -6.49555590e-01 6.17008667e-02 -1.45849211e-01 3.67354832e-01
319 | 9.42305967e-02 7.23617193e-01 8.29936776e-01 4.36680120e-01
320 | -1.34880643e-01 -4.74233070e-01 -1.07046471e+00 1.41745762e-01
321 | -3.61004662e-01 -3.65177556e-01 -5.75614517e-01 -3.62845828e-01
322 | 5.55116484e-01 7.09662507e-01 3.65018814e-01 -3.93567893e-01
323 | -2.51527720e-01 -6.57455245e-01 -6.78287975e-01 1.18949804e-01
324 | -9.65716123e-02 -9.34022723e-01 1.73058750e-02 -3.88506002e-01
325 | 3.75363501e-01 -6.44814160e-01 -2.40348064e-01 1.63501506e-01
326 | 2.52122119e-01 1.20209845e-01 7.42398324e-01 -1.56696483e-01
327 | 6.74058387e-01 4.31197062e-01 -1.91793370e-01 -6.68223273e-01
328 | 3.40425420e-01 -5.43554059e-02 -2.64529923e-02 2.46006402e-01
329 | -9.64234132e-01 1.93711415e-01 2.40424197e-01 -2.20065047e-02
330 | -1.53943726e-02 4.12644416e-01 -2.96496559e-01 8.13254881e-01
331 | 9.83606665e-02 1.57152805e-04 -2.59666973e-01 -1.50174301e-01
332 | -1.93334234e-01 -3.36786002e-01 -2.46173283e-02 3.22940908e-02
333 | 2.20827355e-01 -3.11695003e-01 -8.09754527e-02 -2.13715678e-01
334 | 1.14247308e-01 -3.63763969e-02 -5.19081645e-01 -5.74958867e-01
335 | -1.63681612e-02 -3.83685192e-02 -6.76858427e-01 2.26416241e-01
336 | -1.59645694e-01 1.22535956e-01 -2.72075497e-01 -3.01182881e-01
337 | -4.98412116e-01 -4.55792333e-01 -8.89425173e-01 3.48325186e-02
338 | -2.24174196e-01 -5.32393742e-01 2.72066790e-01 -2.60936614e-01
339 | -2.44543116e-01 -6.85346213e-01 1.82116954e-01 3.49315642e-01
340 | 3.44882211e-01 1.31232242e-01 -3.53086604e-01 -1.30674833e-01
341 | -5.88434905e-01 -2.48650104e-01 -3.52925995e-01 3.25956316e-01
342 | -1.63528821e-01 5.54554172e-01 -2.10367026e-01 -6.60545825e-01
343 | -3.38318624e-01 1.89968528e-01 3.99156935e-01 -7.47114933e-02
344 | 3.47766881e-01 5.01630680e-01 -4.36138658e-01 8.04772351e-01
345 | 4.98336851e-01 2.89819130e-01 -1.14740712e-01 -2.51069776e-01
346 | -1.05330850e-02 4.39131499e-01 8.25728129e-01 -8.12114865e-01
347 | 6.52095010e-01 -8.57809123e-01 3.38061301e-01 -9.09466974e-01
348 | -8.05000191e-01 6.11627873e-01 -3.16850439e-01 -2.05907779e-01
349 | 9.23088656e-02 4.29348054e-01 -7.02252162e-01 -1.88325346e-01
350 | 1.79371886e-01 -9.65236301e-02 4.23764930e-01 -3.60337745e-01
351 | 1.31675108e-01 -2.20307860e-01 -8.97772984e-01 -6.68524043e-01
352 | 5.31979799e-01 1.95296264e-01 -4.47548406e-01 3.01227291e-01
353 | 8.65932153e-01 1.21185959e-01 -5.61956226e-01 3.69672604e-02
354 | -2.42731995e-01 3.07672484e-01 -5.87463532e-01 3.86918350e-01
355 | 2.59231209e-01 -6.51007653e-01 -3.77576856e-01]
356 | 2020-04-21 21:55:44,754 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
357 | 2020-04-21 21:55:44,976 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.7246902777440856, best pos: [-0.10699094 0.31788444 -0.3141306 -0.12500852 -0.26096274 0.41158214
358 | 0.00480221 -0.38098164 0.04982969 -0.1742194 0.02643746 0.98273
359 | 0.39400664 0.07268601 0.20208297 -0.16730082 0.2171741 -0.00384403
360 | 0.45671741 0.17705053 -0.15875144 0.39784954 -0.09879032 0.29836467
361 | -0.22637539 -0.66349679 0.47184063 -0.0167074 -0.38214405 0.24009111
362 | 0.55230306 0.31645622 0.72012231 0.29296162 0.07222997 0.44508187
363 | 0.04126998 0.10805999 1.13961965 -0.6130157 -0.41991593 0.23956251
364 | -0.05915109 0.78414944 0.02699128 -0.30122692 -1.07214438 0.81827838
365 | 0.12688685 0.0195484 0.24919342 0.1735588 0.21769905 0.45980954
366 | -0.16824002 0.31600386 0.46607468 0.30058293 -0.10365249 0.60542771
367 | -0.0359663 -0.06660156 0.09951927 0.21405337 0.75174361 -0.54871386
368 | -1.03470603 -0.11796134 0.09752361 -0.0047474 0.26795866 0.64885092
369 | -0.71036179 0.3398746 -0.10126676 0.01689118 0.06192061 0.09325649
370 | -0.66000407 -0.03049113 0.65645591 -0.35579815 -0.00414037 0.10965392
371 | -0.37429819 0.40626949 -0.09617873 -0.2863238 0.19927598 -0.09084816
372 | -0.41967156 0.22942834 -0.13455443 0.35938302 0.02332039 -0.49357189
373 | -0.41333557 -0.30509182 0.5682194 0.19461246 0.27869492 -0.21275596
374 | -0.46539226 -0.5246865 -0.48395972 -0.17320338 -0.59044296 0.21184648
375 | 0.63619796 0.40263036 0.51888152 0.79715017 0.06002348 -0.23228863
376 | 0.75134506 -0.10641314 -0.319861 -0.25936862 0.37369673 -0.61821666
377 | -0.76882579 -0.67401769 0.04033938 0.57881678 -0.72630277 0.20348946
378 | 0.09371444 0.52612451 0.04069944 0.5137937 -0.63974186 0.42601609
379 | 0.40643013 0.24755123 -0.14338753 -0.60643025 0.48378635 -0.12208243
380 | -0.25417591 -0.60743478 0.11245901 0.43779347 0.09680933 -0.00794548
381 | -0.29139656 0.47190814 -0.51902887 -0.23123414 0.17412572 0.39783093
382 | 0.70323796 -0.14103604 0.52146504 0.26074358 0.01771413 0.25761442
383 | 0.31415253 -0.11324659 0.01808715 0.80079085 0.53337599 0.50324064
384 | 0.01156146]
385 | 2020-04-21 21:55:59,472 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
386 | 2020-04-21 21:55:59,701 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.7086959916736106, best pos: [ 0.50939887 -0.24472308 0.19982522 -0.11286561 -0.09856422 0.59997962
387 | -0.85444819 0.09464106 0.4891656 0.05029447 -0.02513431 -0.0356334
388 | -0.32686588 -0.74225539 -0.23442924 0.64053991 -1.80556036 0.16992884
389 | 0.47325219 -0.26783264 -0.45536346 -0.52465212 -0.04949751 0.51383431
390 | -0.21700044 -0.05876942 -0.50930314 -0.34928111 -0.6176022 -0.64753073
391 | -1.00624641 -0.12733813 -0.01803956 -0.47880578 0.51476016 0.13400125
392 | -0.87247426 0.56359832 0.54712659 -0.9300406 0.36703106 0.39392003
393 | -0.58648798 -0.26569122 -0.28620231 -0.61546724 0.52367075 0.08627017
394 | -0.38451 0.10864754 0.11096316 0.04930571 0.71389918 0.80065824
395 | -0.1314543 0.33093175 -0.55260191 0.28884605 -0.50546535 0.52909482
396 | -0.04351237 0.35748457 -0.35207187 0.70033404 0.47920256 -0.1821964
397 | -0.46141368 0.42173422 0.05014824 -0.6417694 0.53209414 0.21672522
398 | -0.91312856 0.71325061 -0.08232918 0.07581838 0.21027183 -0.49808232
399 | -0.42123498 -0.73009223 -0.52813458 -1.28241326 -0.76391738 0.22213243
400 | 0.43579782 -0.34839712 -0.56335179 -0.56543659 -0.15856516 0.37858915
401 | -0.43930739 0.52416415 -0.20786365 -0.43056194 0.15205248 -0.17164129
402 | -0.47868181 0.15450892 -0.37823873 0.03833777 0.44814759 0.99387325
403 | 0.55344484 0.1984895 0.63509399 0.4136837 0.217681 -0.05197068
404 | -0.08281223 0.23472681 -0.30223779 0.68294815 0.93705297 -0.52027622
405 | 0.1215561 0.42098169 -0.13543025 -0.63204269 -0.40053192 0.70295229
406 | 0.60604063 -0.99416434 0.53551556 0.37409344 0.22033298 -0.14418101
407 | -0.75726528 -0.82458051 0.16730126 -0.48534186 0.54864164 -0.14719589
408 | 0.072439 -0.22534213 0.32579036 0.58752324 0.09762115 -0.89617982
409 | -0.50870136 -0.70793012 -0.10550751 0.27646216 -0.44564522 0.06899438
410 | -0.26247529 -0.25142558 -0.2181641 0.12534381 0.17157383 -0.97902254
411 | -0.08001609 -0.76410582 0.18659248 0.31118396 0.22860523 0.65522902
412 | 0.15807191 -1.0599801 0.28524061 0.57262242 -0.71937373 -0.54818141
413 | -0.58899107]
414 | 2020-04-21 21:56:12,048 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
415 | 2020-04-21 21:56:12,273 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.8854742847509677, best pos: [-0.07839968 0.08344974 -0.65714929 0.05828523 0.05947699 -0.68029912
416 | -0.26014619 -1.0632867 -0.78346192 -0.05999885 -0.24630766 -0.44876281
417 | 0.18561922 -0.4724675 0.28932959 -0.01282625 0.014816 0.33061893
418 | -0.46843343 0.02409155 -0.11704097 0.04349188 -0.11184823 -0.37940282
419 | -0.53033124 -0.14963725 0.036977 -0.2032801 -0.53861634 0.14127485
420 | -0.57544231 0.32624228 0.51904962 -0.47328904 -0.33115324 -0.97360773
421 | -0.29849945 0.17877214 0.07224507 -0.65745328 -0.47009432 -0.18722612
422 | -0.39449786 0.24168948 -0.09666673 -0.19748253 -0.05759966 -0.28225375
423 | -0.35732679 0.19543867 0.00935963 -0.48253569 -1.28409608 -0.39246219
424 | -1.1338547 -0.55223868 -0.17847544 -0.43243247 -0.83240748 -0.43932539
425 | -0.35309834 0.44555081 -0.9310592 0.33912388 -0.08812621 0.58040887
426 | -0.89290624 -0.75263691 0.29743446 0.53257342 0.24210951 -0.12013148
427 | -0.20489259 0.28085895 0.3778124 -0.74028276 0.08391707 -0.56244074
428 | -0.52316434 0.50622414 0.1419144 -0.82259731 -0.76030893 0.40997932
429 | -0.02531164 -0.44008464 0.14948191 0.65162595 0.05174413 -1.39206676
430 | 0.64273536 -0.37897127 -0.35471318 0.06836506 0.44654467 0.62489388
431 | -0.41804947 -0.05915674 -0.09534193 -0.29106517 -0.48313726 -0.79409098
432 | 0.18522397 -0.63341277 0.13224664 0.30543071 -1.02352373 0.2592089
433 | -0.34866246 -0.55347664 -1.12396311 0.18986045 -0.02157318 -0.53494117
434 | -0.20654964 0.33560673 -0.062987 -0.80849035 -0.22297362 0.36224339
435 | 0.64695048 -0.22041977 -1.11621103 0.3276063 -0.07372587 0.25450257
436 | -0.17033034 -1.08812794 -0.58356096 0.34624669 0.03656918 -0.62959822
437 | 0.12379377 0.05278042 -0.14622902 -0.22348292 0.18809055 0.13850772
438 | -0.0722611 0.29149863 0.35736485 -0.02007193 0.40790361 -0.39459522
439 | -0.4406542 -0.42368777 -0.667164 0.25112828 -0.46460589 -0.73879713
440 | -0.67226447 0.33656672 0.0697306 0.37196962 0.32590984 0.14115522
441 | 0.24775349 -0.11472958 -0.45173422 -0.83096151 -0.09402134 -1.13431841
442 | -1.12051768]
443 | 2020-04-21 21:56:17,395 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
444 | 2020-04-21 21:56:17,619 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.7516584712621366, best pos: [ 0.77014641 0.54846181 0.24237584 -0.15107275 -1.25285473 0.41532505
445 | 0.29405703 -0.71136841 -0.56441276 0.90287074 -0.16486504 -0.64775007
446 | 0.37643579 0.80088753 1.19168824 0.12890146 1.47777271 0.46761738
447 | 0.05474971 0.43876101 -0.65887077 0.53208337 0.80016734 0.7467827
448 | 1.17842729 0.09820768 1.14484236 1.0152647 0.65866946 0.22901606
449 | -0.75236261 1.08379936 0.44962117 0.37114961 0.3173266 0.01096485
450 | 0.83503588 -0.32100258 0.16903072 -0.37650763 0.37397651 0.05605966
451 | 0.05515749 0.53424997 -0.97272008 -0.15261032 -0.19359505 0.54537833
452 | 0.35333092 1.45786899 0.15804318 0.04051367 0.17248075 -0.27457414
453 | 1.12774778 -2.18135895 1.33616816 0.95061017 -0.5817206 0.68838547
454 | -0.30394901 0.72185396 0.98078211 0.53416321 0.30124722 0.48554135
455 | 0.85671953 0.64155324 0.25815994 -0.51957108 1.58762468 -0.11738615
456 | -0.24193726 -0.37625961 0.00323245 -0.32330143 -1.58575568 -0.00293861
457 | 0.73012434 0.25558107 0.12909412 -0.64203465 0.74479452 -0.70144591
458 | -0.54006388 -0.09030291 0.45472525 -0.13160118 0.2024091 0.34174559
459 | 0.63353849 0.78964843 0.13758799 0.84216599 -0.48813223 0.42117866
460 | 0.52586065 0.9451203 0.84631631 -0.01619543 -0.06165902 0.60606994
461 | 0.334798 -0.02336325 -0.61778449 0.27546179 0.38700255 0.04287443
462 | -0.77525912 -0.74947414 0.39228128 -0.22521032 0.22329768 0.37415587
463 | 0.73051437 1.79082804 0.19634366 -0.11391743 0.21232014 0.99679039
464 | 0.74541638 -0.19726532 0.64048546 1.13339004 0.34019261 -0.11161818
465 | 0.55237764 0.24145353 0.50413834 0.79312745 -0.28008881 0.02282462
466 | 0.59689774 1.7965767 -0.04416218 0.58399122 -0.0892443 0.08452858
467 | 0.06871903 0.08062184 0.77662468 0.49722155 0.30446666 0.44592807
468 | 0.90802385 0.02538594 0.27133974 0.26701121 0.08126389 0.2547173
469 | 0.03354937 0.60177073 -0.27773844 -0.53674268 -0.02406065 0.13184327
470 | -0.20707925 -0.25406351 0.67444971 -0.11819762 -0.37550836 -0.02707018
471 | 1.05480649]
472 | 2020-04-21 21:58:12,558 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
473 | 2020-04-21 21:58:12,767 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.5095230208739963, best pos: [-0.03426882 0.16997632 1.38533317 -0.44196096 0.83517923 0.39984456
474 | -0.44216578 1.93778086 -0.21261401 1.20731332 -0.59982395 -0.23654712
475 | 0.63086261 0.11044719 1.74314084 -0.28256291 0.34100347 1.98343498
476 | 0.81082789 1.08393797 -1.07652242 1.79170571 -0.48147052 0.90037151
477 | 0.06721876 0.97391287 0.24598348 0.77360113 0.63888972 1.74844014
478 | 1.44087769 -0.14018525 1.15633615 0.38193644 -0.10002153 0.48322677
479 | -0.23695041 0.48548418 0.56042324 0.60148187 0.84076729 1.4413242
480 | 0.54157429 0.14893105 0.53664147 0.04791261 0.29062996 1.37987939
481 | 1.01387051 0.88570616 1.26224492 0.59302429 1.68242931 -0.67347852
482 | -0.04685897 0.56782073 0.39804646 -0.36340731 0.88521541 0.96917048
483 | 1.59048259 0.21746334 0.93299196 0.49123598 0.18800518 -0.63353084
484 | 0.35638057 -0.05007042 1.34696105 0.04757399 0.35794632 -0.02617787
485 | 0.94266915 1.3114866 0.48906979 0.61947756 1.43885728 0.44786201
486 | 1.47239966 1.83698927 1.04269512 1.10601639 1.92189389 0.71551743
487 | 1.42007887 1.10042947 0.68788507 1.68234272 0.51459755 1.32625409
488 | 0.71970173 0.45373134 -0.46766609 -0.35302835 1.19431602 0.29624881
489 | 0.7359919 1.01929153 0.92865118 0.29053135 -1.03429465 2.17093355
490 | 1.13243843 1.10560532 -0.38221253 0.31634779 -0.81857898 0.84814442
491 | 0.30165216 1.65358488 0.42053009 0.55999575 0.48591048 0.32347548
492 | 0.24345383 -0.02491122 0.94934382 0.08156269 0.55267401 0.06500365
493 | 0.73875212 -0.90604914 1.49469005 0.82787957 0.7575369 0.03471026
494 | -1.40179748 1.4421582 -0.40043477 0.66806136 -0.56942186 0.05327695
495 | 0.11344747 0.29919402 0.81775118 0.46889 0.64619703 0.72845119
496 | 0.77377084 1.5265439 -0.26327366 0.21675002 1.43441217 0.93287078
497 | 0.44580216 0.9790037 0.54447623 0.67676508 1.10995633 -0.13830065
498 | 0.95028387 0.0534468 0.80637755 1.3875088 0.96685253 1.05814289
499 | 1.80974448 1.17603958 0.12514403 0.21531236 -0.6847808 0.69952091
500 | 0.77997402]
501 | 2020-04-21 21:58:51,716 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
502 | 2020-04-21 21:58:51,951 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.6133864389201987, best pos: [ 1.13119096e+00 -3.69075451e-01 4.84165548e-01 6.66772448e-01
503 | 5.74416542e-01 9.41858573e-01 6.53572323e-02 2.19771659e-01
504 | 4.29966932e-01 7.80049743e-02 3.06385374e-01 -1.12071990e-01
505 | -6.31063173e-01 6.77939020e-01 2.84835973e-01 -5.41906327e-03
506 | -1.05514406e-01 -2.74388346e-01 -1.92381620e-01 5.45902325e-01
507 | 4.43277803e-01 -2.87125911e-01 8.83012580e-01 4.24704437e-01
508 | 3.39678704e-01 7.29639650e-01 1.12614929e+00 -1.84682214e-03
509 | 1.08673953e-01 -6.16143402e-01 7.20892164e-01 -1.44735618e-01
510 | -1.49367828e-01 2.49908379e-01 1.41091422e-02 4.16477744e-01
511 | 5.31804058e-01 -3.64849072e-01 2.19629627e-01 -2.43792425e-01
512 | -5.43340949e-01 6.31687341e-01 2.36332437e-01 9.00528732e-01
513 | 1.73690449e-01 4.40413487e-01 6.37232949e-01 8.60383652e-01
514 | 5.70422267e-01 6.05033937e-01 4.49883371e-01 3.62983498e-01
515 | -2.03961702e-01 3.80970578e-01 2.16696865e-01 -1.27042705e-01
516 | -6.20130416e-01 7.83148574e-02 4.46564210e-02 1.04556579e+00
517 | 1.22659674e+00 2.77302272e-01 2.34744164e-01 1.09689662e-01
518 | 2.16501008e-01 9.25524654e-01 7.79741878e-01 1.43974445e-01
519 | 2.17599782e-01 8.09706883e-01 1.49293941e-01 8.83432406e-01
520 | 1.09572503e+00 -1.78748514e-01 5.94218614e-01 2.96070974e-01
521 | 5.77669770e-01 -5.28592244e-03 -2.63751884e-02 4.41832387e-01
522 | 5.42872044e-01 5.82282194e-01 3.37646352e-01 6.18663073e-01
523 | 1.36898892e-01 -5.93061046e-02 -2.87596789e-01 -9.76925281e-02
524 | -1.82709214e-01 -4.51571050e-01 1.06503007e-01 -1.75889540e-02
525 | 1.10560587e-01 9.03792656e-01 2.35529398e-01 3.48276866e-01
526 | 1.05949170e-02 3.43388062e-01 1.43127575e-01 1.29251942e-01
527 | 5.70768311e-01 4.77296498e-02 4.18001131e-01 -1.65734039e-02
528 | 3.55720958e-01 3.34286639e-01 8.89463888e-01 2.90000280e-01
529 | 2.68798964e-01 -4.56910836e-01 -1.71309879e-01 -6.75761223e-03
530 | 7.10960623e-01 1.84358300e-01 -4.09473337e-01 1.68900374e-01
531 | 1.25260243e-01 3.32061722e-01 4.58296997e-01 7.29941287e-01
532 | -1.59462450e-01 1.26325270e-01 8.63366687e-01 6.04598763e-01
533 | -1.04214785e+00 4.30774394e-01 -7.47311039e-02 -5.93538083e-01
534 | 6.55823874e-01 3.24332229e-01 -3.30662205e-01 2.07202560e-01
535 | 8.01119345e-01 4.80419365e-01 7.96805630e-01 9.88980893e-01
536 | -1.54720105e-01 9.13965777e-01 8.47126735e-01 2.15283460e-01
537 | 1.21468638e-01 -1.55263291e-01 1.43637927e-01 4.52655543e-01
538 | -3.42397784e-02 9.68271913e-01 5.19821811e-01 3.87250085e-01
539 | 8.57669570e-01 4.32725178e-01 3.08303809e-01 6.60678041e-01
540 | 1.37608719e+00 5.85478106e-02 3.37618296e-01 3.32572938e-01
541 | 1.99701804e-04 4.58011258e-01 -1.50599775e-02 6.04918711e-01
542 | 4.34291770e-01 8.63142308e-02 1.03028409e-01]
543 | 2020-04-21 21:59:01,017 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
544 | 2020-04-21 21:59:05,755 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
545 | 2020-04-21 21:59:05,973 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.702456788546799, best pos: [-0.18059022 -1.04170296 0.09069561 -0.59670083 -0.35373947 -0.06526648
546 | 0.76004964 -1.30933492 -0.16046417 -0.57894807 0.00554564 0.4464132
547 | 0.36356047 0.55347959 -0.09832267 -0.23249534 0.06718439 0.19960742
548 | -0.04546447 0.19673913 0.08506578 -1.38804743 -0.67586678 0.06933929
549 | 0.19305506 0.30115543 0.32308618 0.46752608 0.14712721 -0.14252227
550 | 0.57960472 -0.69247227 0.6448436 0.60368508 -0.23400891 -0.17143076
551 | -0.61619015 0.76547579 0.29828356 0.87262854 0.02268925 0.51791616
552 | 0.46003885 0.52553784 0.69674566 0.25849605 -0.08595731 -1.04596498
553 | 0.88960023 -0.01110274 0.22982715 -0.5609281 0.22674005 -0.1796442
554 | 0.204864 0.66710323 0.66802016 0.41302715 -0.06072067 -0.2428236
555 | 0.84462349 -0.16325382 0.13200106 0.30817073 0.30433922 -0.18815541
556 | 0.03285722 0.51939292 -0.69987001 -0.09861971 0.2166521 -0.47784105
557 | -0.30095549 0.48405709 0.04549055 0.31296583 -0.51531895 0.03737348
558 | -0.73437802 0.24876649 0.03262572 -0.21204286 -0.16511248 -0.18271069
559 | -0.04170872 -0.22982791 -0.08304859 -0.1116794 0.11986084 0.51001882
560 | -0.03648056 0.59481836 0.61131331 -0.11169366 0.34113119 0.70118057
561 | -0.14549762 0.24703478 0.03653448 -0.18293335 0.09226176 0.29984611
562 | 0.2876624 0.42057281 -0.27526274 -0.50841119 0.20067527 0.24739463
563 | 1.06908397 -0.48583477 0.11957021 -0.40324267 -0.54678067 0.22498902
564 | -0.04777549 0.43528566 0.77136777 -0.56654604 0.41234631 -0.55342865
565 | 0.35508515 0.8988459 -0.40279524 -0.19759115 0.30212922 0.75647909
566 | 0.22604658 0.01714775 0.13253807 -0.26952243 0.0264359 -0.1979524
567 | -0.03007151 0.13163881 0.06665326 0.37157874 0.38579923 0.22411679
568 | 0.18048079 0.39964504 0.01514774 0.14759697 0.00400568 -0.67021801
569 | -0.18196916 -0.12776617 0.32018738 0.44085942 -1.10696875 0.35974646
570 | 0.29569197 0.45509234 0.00482479 0.2308986 -0.55187271 0.36375848
571 | 0.08493949 -0.28897347 0.11612063 -0.07843989 0.4799757 0.13161021
572 | -0.13751753]
573 | 2020-04-21 21:59:54,415 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
574 | 2020-04-21 21:59:54,647 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.8923812264618319, best pos: [ 0.84264332 0.03747982 0.93024278 0.59997203 0.50131329 0.56019619
575 | -1.2136356 0.7451615 0.66976489 0.05170085 -0.15371211 1.01295034
576 | -0.15858945 -0.01897607 -0.73593558 -0.06762628 -0.25980541 0.55760903
577 | 0.86310051 0.70339568 0.57374202 -0.11535366 -0.29638261 0.60153332
578 | 0.61946376 0.144974 0.71145076 0.44291017 -0.52804493 -0.7895944
579 | -0.46125067 0.98275047 -0.3515816 0.54998642 0.93128282 0.59793915
580 | 0.895288 1.34043831 1.1605129 0.02279668 1.0793535 0.76397256
581 | 0.57623124 0.49155231 1.0189334 0.79330649 0.36935785 -0.51297775
582 | 0.07001334 1.01793632 0.1004681 0.13758601 0.45851688 1.21974931
583 | 0.43711059 0.50963566 0.80279004 -0.59541104 0.1363996 0.66873363
584 | -0.39108948 0.27483418 0.44424945 0.29541828 0.34065824 0.80903954
585 | 1.06270546 0.40845729 1.44517483 1.50563954 -1.02906191 -0.19687459
586 | 1.17607654 0.38116348 -0.45298725 0.16738923 1.5042181 0.2460555
587 | 0.82550696 0.50362898 0.30631308 -0.03603221 0.67217797 0.73021557
588 | 1.03858847 -0.06620196 0.37158111 1.61130143 0.77575071 0.69975979
589 | 1.02051961 -0.36531102 0.59096571 0.28054434 -0.73288282 0.46601425
590 | -0.1737645 0.46287899 0.79856273 0.75984016 0.86830647 0.44962464
591 | -0.09937043 0.42322279 -0.57008569 0.84425049 0.04778946 0.97939176
592 | 0.32568605 -0.13889178 -0.41982348 0.44727149 -0.16049338 0.99132225
593 | 0.98910711 0.66840934 1.00117021 0.33933058 -0.32124082 0.18723619
594 | 0.56169402 0.7954105 0.81673054 0.36420331 1.0861539 -0.46086697
595 | -0.00601692 0.15393664 0.61233392 0.22556244 0.28582888 0.80745711
596 | 0.69907132 1.06450693 0.97929988 0.07530287 0.01100352 0.55770548
597 | 0.74362998 0.02446214 0.60900154 0.30727037 1.07877692 0.02347125
598 | 0.4950208 -0.42810311 -0.41269195 -0.09206728 0.19751885 -0.07669153
599 | 0.76972769 0.43537211 0.93488354 0.26409135 0.14731751 -0.219297
600 | 0.68335448 0.22789821 0.48495488 0.57052418 0.1542558 0.50695413
601 | 0.11660408]
602 | 2020-04-21 22:01:24,721 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
603 | 2020-04-21 22:01:24,933 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.8750729072124871, best pos: [ 0.30931318 0.24766199 -0.1741799 0.35318488 -0.38226281 0.56135309
604 | -0.15574684 0.48916785 -0.72788195 0.64753748 0.11525685 -0.31155697
605 | 0.02459842 -0.13591427 -0.50762966 0.24954846 0.31063224 0.22693555
606 | -0.62912534 0.44005447 -0.15491656 -0.64677407 -0.02594678 0.48377622
607 | 0.67311256 -0.66032322 -0.32362133 0.32658033 1.05409193 0.41141435
608 | 0.32613096 0.61577801 0.69297214 0.15237884 0.11581924 -0.2731561
609 | -0.18990249 -0.73271327 0.15629994 -0.66745107 0.19807373 0.18747091
610 | 0.07098504 -0.37090675 0.16138215 0.21215798 0.92989878 -0.10097436
611 | 0.13292226 0.12781436 -0.38309722 -0.18658116 0.37671488 0.00991849
612 | -0.35418277 0.43523415 -0.23000822 0.4174899 0.78879799 0.68751825
613 | -0.48464903 0.35801252 0.1449004 -0.46373279 0.77949334 1.04530491
614 | 0.08241872 0.11104065 0.96994879 0.63448953 -0.44115192 0.3360534
615 | 0.52347081 0.48592748 0.3266636 0.98780938 0.20687005 0.3454879
616 | 0.38900495 0.01728163 0.65953107 -0.27458726 -0.16016949 -0.47410254
617 | 0.21656131 0.42216764 0.14412724 -0.11759919 0.51980024 0.97041683
618 | -0.08326168 0.58011196 -0.30135807 0.68246581 0.88282276 0.55566661
619 | 0.43459299 0.30758844 0.93733511 -0.69511989 -0.22910169 0.6539017
620 | 0.18457305 0.347168 0.23201688 0.8446272 0.02286936 0.47699426
621 | 0.9653544 0.58201561 0.15659865 0.10421815 0.38691704 0.43661149
622 | 0.12526559 -0.0437279 0.19931062 0.29924957 -0.12284923 0.71920699
623 | -0.53103846 -0.16182086 -0.58247393 0.06297792 0.60854549 -0.44596311
624 | 0.50403899 0.59797904 0.14268981 0.56819418 1.03861989 0.18351432
625 | -0.36200724 -0.58657254 -0.02721088 0.62713198 1.12098101 0.21023895
626 | 0.32116511 0.77175568 -0.46858383 0.91423129 0.32807212 0.88415009
627 | 1.02196053 0.78556245 0.84039457 1.08264425 -0.09008111 0.75571139
628 | -0.279954 0.17780064 0.33660694 0.03861011 0.44570016 0.44568956
629 | 0.75077357 -0.35235813 -0.25792098 0.08514727 -0.1106723 0.30228808
630 | 0.66272813]
631 | 2020-04-21 22:01:37,402 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
632 | 2020-04-21 22:01:37,634 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.7803686975989826, best pos: [ 0.72724629 0.26871517 0.32382005 0.21570412 0.76016358 0.76223393
633 | 0.42062659 -0.1154922 0.05487386 -0.04879092 0.54815328 -0.10400792
634 | 0.30779115 0.25769213 -0.05877574 0.3354615 0.29556316 0.36026516
635 | 0.70177486 0.20406195 0.53002984 0.32880907 0.50207325 0.47146507
636 | 0.4643261 -0.14326158 0.65863912 0.22704065 -0.07079922 0.20128396
637 | 0.12085515 -0.39141589 0.2303447 -0.15305354 0.29930362 0.29329169
638 | 0.49441931 0.23879193 0.16822625 1.33037373 0.30244491 0.4663166
639 | 0.04985885 0.22182059 0.36846778 1.10829613 1.17846342 0.56080441
640 | 0.94121842 0.16572205 0.21416901 0.52994218 1.36845975 0.32495104
641 | -0.36006898 0.0071118 -0.09497646 0.38532475 0.73465537 0.21424035
642 | 1.35070488 0.04922341 0.63272362 0.37924519 1.22675667 0.39185094
643 | 0.49692739 0.59390938 0.3459916 0.32197988 0.24196166 0.53646291
644 | -0.1621737 0.65475391 -0.11937073 0.11385158 0.69121874 0.01414549
645 | 0.51739184 0.71042837 0.32991502 0.61387319 0.65928477 0.94185501
646 | 0.73991237 0.89432587 0.38984831 0.61657029 0.27798439 0.44201674
647 | -0.71279758 0.91478647 0.64824619 0.18002215 0.61103989 0.35751335
648 | 0.61003624 0.52539402 0.31196726 0.70627918 0.33019047 0.57561704
649 | 0.00882989 0.36572245 0.4603024 -0.05349499 0.50251676 0.86671371
650 | 0.88868952 0.54965825 -0.22386199 -0.1620077 0.4600452 0.27396425
651 | 0.87073013 0.10022215 -0.02855191 0.37891422 0.87920811 0.00802582
652 | 0.03664048 0.43265306 0.45005086 -0.24602673 0.70640152 0.10523054
653 | 1.52136847 -0.0152901 0.92772968 0.69113464 0.49820319 0.69247005
654 | 0.80518889 -0.60998823 0.32111448 0.11522085 0.96541955 0.44543483
655 | 0.60106718 0.19189278 0.88474865 0.51534395 0.58337439 -0.00502371
656 | -0.19093824 0.9535479 0.77561928 0.82833244 0.48143761 0.93925958
657 | 0.21268653 0.8240627 0.64590994 0.73385128 0.51602415 0.17995383
658 | 0.6339538 0.07148455 0.81884284 0.54956967 0.62878755 0.50743958
659 | 0.56909599]
660 | 2020-04-21 22:03:13,721 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
661 | 2020-04-21 22:03:13,960 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.7698480169099977, best pos: [-0.5111821 0.39515216 0.30592061 0.12954558 -0.19252431 -0.33364998
662 | -0.2288595 -0.40677572 0.47451705 -1.20135205 -0.26437762 -0.28122418
663 | -0.03358137 0.1327182 -0.15085272 0.09294626 0.13801527 0.34136747
664 | -0.18055059 0.06049109 0.18965621 -0.27010328 0.05000275 -0.24172958
665 | -0.07175939 0.12821145 -0.39749418 -1.01324324 -0.314675 0.61760428
666 | -0.23442279 -0.14361072 -0.48488769 0.08761715 0.85363152 -0.38998514
667 | -0.28445489 0.04453939 -0.04988005 -0.28807076 0.16338432 -0.220583
668 | -0.18806694 0.05127113 -0.20578772 0.05013472 -0.56806999 -0.11629891
669 | 0.4205497 -1.74073304 -0.03566301 0.74876656 0.30208757 0.39872055
670 | 0.37794232 0.28780756 -0.11895146 0.24462698 -1.16752656 0.20699533
671 | -0.73723275 0.34046519 0.33444035 0.43736906 0.43988908 0.45848232
672 | 0.03577201 -0.41600012 -0.03494154 0.06468057 -0.21146916 0.29959166
673 | -0.93009981 -0.24349032 0.31789095 -1.12010593 -1.08232894 0.034054
674 | 0.26836383 0.21315764 -0.04981694 0.34909876 0.16780528 -0.0516662
675 | -0.11161764 0.52459011 0.47674035 -0.29655972 0.27497868 0.13415885
676 | -0.28694967 0.01002219 0.13978558 -0.3999142 0.46308962 -0.21514417
677 | -0.57125071 -0.57389513 0.12430985 -0.5384023 -1.01421451 0.44201852
678 | 0.00938867 0.32456092 0.83650077 -0.187729 -0.86478298 -0.46235792
679 | -0.43669352 -0.59756316 -0.57744928 0.52604628 0.37104107 0.45226713
680 | 0.11136081 -0.07118333 0.41858616 0.06898719 -0.93575712 -0.07724166
681 | -0.38835683 0.11768296 0.00787631 -0.3633318 -0.5612771 -0.08657999
682 | -0.47309844 -0.47293841 -0.15595595 -0.03738569 0.40727835 0.37946041
683 | -0.2471418 -0.26447625 0.19028959 0.64127323 -0.79862551 0.18847449
684 | 0.10095962 -0.42106884 -0.25034627 -0.06366187 -0.86192537 0.00786764
685 | 0.01749614 -0.40170406 -0.13270508 0.22369026 0.11147203 0.02950581
686 | -0.36097924 0.78489762 0.6506442 -0.21021144 0.03846284 -0.57162222
687 | 0.37581306 -0.01268017 0.21752307 0.51561363 -0.28898333 0.27647758
688 | -0.05461401]
689 | 2020-04-21 22:03:56,129 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
690 | 2020-04-21 22:03:58,497 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 0.8762368468666788, best pos: [ 2.82287144e-01 -3.25778747e-01 2.15377233e-01 -1.55163438e-01
691 | -1.72589540e-01 4.59382358e-02 3.72009288e-01 1.42600946e+00
692 | 7.96942267e-01 1.15131436e-01 7.97728314e-01 1.66080609e-01
693 | -5.64137923e-01 6.31240740e-01 -4.24779031e-01 2.21435683e-01
694 | 3.95904565e-01 2.19722264e-01 -2.28409850e-01 9.48449823e-01
695 | 4.42272613e-01 -2.22319890e-01 -5.43395676e-02 2.90322269e-01
696 | 3.55988185e-01 2.35359418e-01 2.78616498e-01 6.29750988e-01
697 | -6.24384863e-01 -4.52782008e-01 2.87321532e-01 3.49783963e-01
698 | 3.63214057e-01 -5.56969102e-01 1.55179569e-01 -1.18639012e-02
699 | 7.65906568e-01 3.58213513e-01 1.27639656e+00 1.46277363e-02
700 | 1.09748329e+00 -1.68076720e-01 4.70807578e-01 -5.85855611e-01
701 | -3.26774276e-01 3.59289903e-01 3.65778871e-01 3.05045755e-01
702 | 3.43744561e-01 -4.61878452e-02 8.63313068e-01 6.13453012e-01
703 | 9.39454581e-02 3.07547284e-01 2.52588755e-01 6.35001149e-01
704 | -1.19618489e+00 6.40707360e-01 -7.25023294e-01 9.02690197e-01
705 | 7.11412004e-01 1.49381519e-01 -2.84268860e-02 -2.26581707e-01
706 | -2.78130292e-01 4.01814077e-01 1.21674055e-01 5.65824896e-01
707 | -1.41000558e-01 -2.24578149e-01 4.65322080e-01 4.64874858e-01
708 | 1.71810340e-01 4.04409086e-01 4.90457122e-01 6.62718886e-01
709 | 3.41168868e-01 1.16123508e-01 4.73878424e-01 7.53726376e-01
710 | -3.23679085e-01 3.40992975e-01 2.20289427e-01 -8.65530712e-01
711 | 5.99732837e-01 2.59219480e-01 8.98742087e-01 5.29935897e-01
712 | 1.51850280e-01 5.67158076e-02 3.08718483e-01 4.91460614e-01
713 | -2.74520604e-01 -1.42753210e-01 -1.85435577e-01 2.27380057e-01
714 | -3.67954070e-01 1.72836074e-01 -2.87139542e-01 9.58782325e-04
715 | 3.56766943e-01 2.39606171e-01 4.18675579e-01 2.45584844e-01
716 | -3.91225745e-02 9.48497792e-03 -3.49564793e-01 1.03613174e-02
717 | 5.44232167e-01 6.13999546e-01 -4.42577520e-01 4.34602077e-01
718 | 9.50706259e-01 3.76176764e-01 -4.53680811e-01 6.46655092e-01
719 | -5.38334480e-03 4.37472114e-01 3.17396704e-01 7.68168289e-01
720 | 6.63455380e-01 5.87652283e-01 -2.61556937e-01 6.51260751e-01
721 | 6.23789220e-01 4.83700807e-02 7.77834884e-02 4.90617012e-02
722 | -8.79453460e-03 7.17782043e-01 3.98725431e-01 2.95961062e-01
723 | 4.78886277e-01 1.39433685e-01 5.56076496e-01 2.62734257e-01
724 | -3.87936380e-01 -6.51042786e-02 -7.42119440e-02 6.52595013e-01
725 | 2.37559038e-01 -9.71998997e-02 -3.96171547e-02 1.90177088e-01
726 | -6.92578682e-03 5.06775085e-01 1.24377106e-01 4.63788204e-01
727 | 6.99601365e-01 3.62029968e-01 7.48869663e-01 3.47961103e-01
728 | -2.17394157e-01 4.93860657e-02 1.15561942e+00 8.35571680e-01
729 | 6.86671751e-01 1.28324486e-01 3.75981636e-02 6.02763318e-01
730 | 4.36861099e-01 8.15698409e-01 1.81113194e-01]
731 | 2020-04-21 22:27:03,324 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
732 | 2020-04-21 22:27:15,016 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
733 | 2020-04-21 22:27:28,301 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
734 | 2020-04-21 22:27:29,648 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.71955795627865, best pos: [ 0.4618805 0.10296898 -0.05543202 1.05309118 1.07825573 0.12561794
735 | 1.08582551 0.65030526 0.18815919 0.71844776]
736 | 2020-04-21 22:27:58,725 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
737 | 2020-04-21 22:28:00,110 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.645562913222474, best pos: [0.40304666 0.11484865 0.95587104 1.28116916 0.88494098 0.2878489
738 | 1.12819162 0.14904106 0.73730842 0.20735439]
739 | 2020-04-21 22:28:04,590 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
740 | 2020-04-21 22:28:05,977 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.98955368128614, best pos: [0.60836409 0.17013103 0.49312554 0.91034029 0.88154269 0.05700724
741 | 0.65635783 0.42224163 0.57818131 1.00262516]
742 | 2020-04-21 22:28:44,605 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
743 | 2020-04-21 22:28:45,245 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.1568781045926, best pos: [0.46500941 0.00322089 1.10840344 0.60348526 0.65581643 0.35220354
744 | 0.92849289 0.21559929 1.31107421 1.16139632]
745 | 2020-04-21 22:29:37,299 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
746 | 2020-04-21 22:30:32,873 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 7.682509631248802, best pos: [ 0.32079921 0.06031847 14.9506492 0.7727968 0.48826477 0.17366699
747 | 1.30624308 -2.78645808 1.03402301 4.72776745]
748 | 2020-04-21 22:31:24,695 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
749 | 2020-04-21 22:31:48,161 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
750 | 2020-04-21 22:32:26,605 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
751 | 2020-04-21 22:32:41,008 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
752 | 2020-04-21 22:33:23,751 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
753 | 2020-04-21 22:34:36,739 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
754 | 2020-04-21 22:35:34,137 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 7.428278176088945, best pos: [ 0.32775393 0.06792599 16.3877132 0.62365251 0.49268856 0.20441852
755 | 2.10177808 -3.43913879 0.39312412 4.5138465 ]
756 | 2020-04-21 22:36:40,624 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
757 | 2020-04-21 22:36:46,616 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 9.85000438766636, best pos: [ 1.83013572e-01 8.26652141e-02 1.05077603e+01 1.01837564e+00
758 | 1.03422473e+00 -2.65755155e-04 1.26763944e+00 5.88154741e-01
759 | 1.75908659e+00 2.27033122e+00]
760 | 2020-04-21 22:40:21,290 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
761 | 2020-04-21 22:40:27,310 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 36842.80774246317, best pos: [ 447.34363471 5.98914918 1295.58672839 380.2879805 144.01029994
762 | 5.80316953 2438.12927641 1138.81259472 1293.21472064 522.96522365]
763 | 2020-04-21 22:40:43,860 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
764 | 2020-04-21 22:40:49,720 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 1537.879080544027, best pos: [ 2.10699308 0.0917433 12.86974664 18.63803212 55.71108044 0.15975191
765 | 24.65497636 59.29557898 21.90599368 66.13127718]
766 | 2020-04-21 22:40:54,043 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
767 | 2020-04-21 22:40:59,989 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.855271089453588, best pos: [0.46975621 0.07384371 0.74045043 0.96204806 0.73939055 0.29596355
768 | 0.79756327 0.76842488 0.65969974 0.85440951]
769 | 2020-04-21 22:41:14,090 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
770 | 2020-04-21 22:41:21,504 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.42359461957811, best pos: [0.61568088 0.09021035 0.13624182 0.96954653 0.62981164 0.25809243
771 | 0.86678328 0.32662883 0.75974411 0.98400908]
772 | 2020-04-21 22:41:36,832 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
773 | 2020-04-21 22:41:43,366 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.186578738361405, best pos: [0.51520692 0.11561565 0.9502386 0.97989949 0.79171221 0.1990786
774 | 0.99585935 0.82355608 0.59093707 0.72498085]
775 | 2020-04-21 22:41:51,339 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
776 | 2020-04-21 22:41:52,003 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.168389764630383, best pos: [0.87092005 0.05636033 0.97609402 0.96748743 0.80181683 0.17147249
777 | 0.49228164 0.97707836 0.84585949 0.98578856]
778 | 2020-04-21 22:42:36,962 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
779 | 2020-04-21 22:42:37,603 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.11036838522396, best pos: [0.92552622 0.01379763 0.9730602 0.98659229 0.52795824 0.35378366
780 | 0.76967137 0.49744454 0.89171266 0.61538904]
781 | 2020-04-21 22:42:47,339 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
782 | 2020-04-21 22:42:48,124 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.578444345915713, best pos: [0.36734783 0.17591426 0.16820372 0.77776222 0.61379456 0.19450885
783 | 0.97577842 0.70497028 0.64560737 0.30881626]
784 | 2020-04-21 22:42:52,671 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
785 | 2020-04-21 22:42:53,390 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 4178.421919398147, best pos: [22.26902155 10.34933843 73.45422225 82.27669041 25.60569156 14.82393037
786 | 81.48356766 33.799295 37.3817432 65.65019945]
787 | 2020-04-21 22:43:23,017 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
788 | 2020-04-21 22:43:23,761 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.435156278525778, best pos: [0.5140614 0.14588794 0.97353489 0.74329077 0.57864212 0.24753748
789 | 0.93825453 0.71821995 0.92426573 0.74779553]
790 | 2020-04-21 22:43:29,949 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
791 | 2020-04-21 22:43:30,567 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.166933074463884, best pos: [0.80337647 0.11137055 0.81375665 0.57497597 0.73070693 0.21431573
792 | 0.30543305 0.83238813 0.51156662 0.98954182]
793 | 2020-04-21 22:43:35,934 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
794 | 2020-04-21 22:43:36,541 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.119095480640663, best pos: [0.20255456 0.06768305 0.14068222 1.73154351 0.67511417 0.37047997
795 | 0.31765407 0.79038996 0.51323405 0.89804536]
796 | 2020-04-21 22:43:42,159 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
797 | 2020-04-21 22:43:47,931 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 12.21708951458308, best pos: [0.1459777 0.05691924 1.51369457 1.13313882 0.82853072 0.23474923
798 | 0.73227151 0.65600729 2.00431478 4.2082932 ]
799 | 2020-04-21 22:47:39,241 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
800 | 2020-04-21 22:47:39,925 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 173044.317793318, best pos: [2459.95332647 332.50928288 2118.99802435 2921.35901233 927.40018946
801 | 171.07742879 2346.37274913 1642.21332885 6687.75931402 284.98547096]
802 | 2020-04-21 22:48:29,037 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
803 | 2020-04-21 22:48:29,683 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 234937.62266343387, best pos: [4590.2749017 58.73319077 4344.22823699 662.78975482 149.84471064
804 | 702.5864643 6018.22883102 6688.7322149 5442.20018333 3310.82235382]
805 | 2020-04-21 22:49:26,442 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
806 | 2020-04-21 22:49:27,092 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 194392.09299894536, best pos: [1903.87869165 256.68801991 2225.14350423 3030.29994892 1937.57655108
807 | 496.07137144 4498.34610801 6604.2601764 2624.01339854 240.90524314]
808 | 2020-04-21 22:52:34,402 - pyswarms.backend.generators - ERROR - generate_swarm() takes an int for n_particles and dimensions and an array for bounds
809 | Traceback (most recent call last):
810 | File "C:\Users\jasonyip184\anaconda3\envs\kepler\lib\site-packages\pyswarms\backend\generators.py", line 65, in generate_swarm
811 | np.all(bounds[0] <= init_pos) and np.all(init_pos <= bounds[1])
812 | TypeError: '<=' not supported between instances of 'int' and 'list'
813 | 2020-04-21 22:52:53,867 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
814 | 2020-04-21 22:52:54,523 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 131862.00152089365, best pos: [ 496.78929878 17.74421408 951.8052729 178.75319361 528.96602248
815 | 858.0994712 4130.18574133 8346.76852527 2079.54528978 1341.51909395]
816 | 2020-04-21 22:52:58,750 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
817 | 2020-04-21 22:52:59,365 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 202029.76393699448, best pos: [1540.92899807 297.33543105 5252.57486045 608.08759337 4549.80939973
818 | 277.47402967 5183.05094382 2133.59340553 2022.16102404 5084.91054616]
819 | 2020-04-21 22:53:09,845 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
820 | 2020-04-21 22:54:14,195 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 102967.79891962555, best pos: [1716.76730603 8.81255004 4131.02760054 1475.59461391 536.95850184
821 | 84.61964251 2717.84050309 2273.37600649 5137.33270135 1316.39553439]
822 | 2020-04-21 22:55:59,431 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
823 | 2020-04-21 22:56:54,636 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.29030973333765, best pos: [0.5485556 0.10080528 0.30919928 0.98012351 0.7852976 0.23793493
824 | 0.86623563 0.67846246 0.97600542 0.85387747]
825 | 2020-04-21 23:00:39,431 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
826 | 2020-04-21 23:01:36,860 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 13.388108884505717, best pos: [0.36162145 0.0639453 1.2694761 1.75291367 1.3189985 0.14579917
827 | 0.9861268 0.92009177 1.73962762 1.13795118]
828 | 2020-04-21 23:03:09,893 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
829 | 2020-04-21 23:04:11,493 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 13.724567241615617, best pos: [0.16417884 0.0510145 3.54210102 0.0507328 0.75126612 0.20088451
830 | 2.14165469 2.84300187 2.0729726 1.67105613]
831 | 2020-04-21 23:05:35,873 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
832 | 2020-04-21 23:06:36,642 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 196.73106051960042, best pos: [ 0.46183839 0.3597003 8.87543557 3.29660901 1.50883355 0.20701209
833 | 12.23011872 5.95339655 13.47681445 8.64127645]
834 | 2020-04-21 23:09:06,092 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
835 | 2020-04-21 23:09:11,986 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.457670544919438, best pos: [0.47839075 0.15718283 0.81883776 0.91128255 0.96294299 0.09111818
836 | 0.55660051 0.8242137 0.53905622 0.77861243]
837 | 2020-04-21 23:14:23,371 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
838 | 2020-04-21 23:14:29,339 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.947116397446653, best pos: [0.64085279 0.08890017 0.53376651 0.95422991 0.68499 0.30254882
839 | 0.81034056 0.40627252 0.66003927 0.98869695]
840 | 2020-04-21 23:18:16,055 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
841 | 2020-04-21 23:18:22,565 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.899215438776185, best pos: [0.53673125 0.11324384 0.75903119 0.78259611 0.72499618 0.20952418
842 | 0.79550551 0.97756087 0.77484156 0.62780549]
843 | 2020-04-21 23:19:04,848 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
844 | 2020-04-21 23:19:10,963 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.202232484640252, best pos: [0.34145285 0.14454253 0.44970898 0.96102294 0.79311589 0.17774129
845 | 0.69665241 0.67316878 0.72724987 0.84040432]
846 | 2020-04-21 23:20:28,111 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
847 | 2020-04-21 23:20:34,180 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.55107670214075, best pos: [0.58857306 0.10497205 0.88304251 0.87399905 0.76413438 0.22651542
848 | 0.80208269 0.74044667 0.74124542 0.80483488]
849 | 2020-04-21 23:21:34,914 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
850 | 2020-04-21 23:22:25,519 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.243691448638963, best pos: [0.53205483 0.07673871 0.67309719 0.98013478 0.6794821 0.29141657
851 | 0.95515013 0.76527119 0.99824604 0.774655 ]
852 | 2020-04-21 23:35:51,211 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
853 | 2020-04-21 23:37:05,357 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.620941393142108, best pos: [0.75398016 0.06889346 0.77048249 0.85597623 0.77509601 0.25728109
854 | 0.79087573 0.94146395 0.91545854 0.82544975]
855 | 2020-04-21 23:48:09,583 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
856 | 2020-04-21 23:55:15,747 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.4537659808413, best pos: [0.57142099 0.10630246 0.84439293 0.96252506 0.64438906 0.25230016
857 | 0.95096954 0.89937313 0.79308057 0.83835898]
858 | 2020-04-22 08:19:27,033 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
859 | 2020-04-22 08:26:06,378 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.136998048747046, best pos: [0.38345847 0.1484685 0.94598758 0.95586836 0.84488016 0.17110159
860 | 0.92367419 0.92993734 0.81870423 0.75798608]
861 | 2020-04-22 09:33:47,064 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
862 | 2020-04-22 09:40:26,747 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.027448742586955, best pos: [0.45089169 0.13541548 0.54706276 0.92227336 0.88642699 0.13841681
863 | 0.90223399 0.88472521 0.92276803 0.91793245]
864 | 2020-04-22 10:33:07,250 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.5}
865 | 2020-04-22 10:35:07,943 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.242402656141124, best pos: [0.4906172 0.11272788 0.2219087 0.97038781 0.89272577 0.23441979
866 | 0.99085626 0.93490658 0.74927424 0.90405071]
867 | 2020-04-22 10:35:07,953 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 1}
868 | 2020-04-22 10:40:05,760 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.1}
869 | 2020-04-22 10:40:32,497 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.082841641176703, best pos: [0.59659394 0.14575562 0.52423474 0.49127502 0.46043385 0.24425332
870 | 0.86643134 0.85887541 0.29879293 0.48982157]
871 | 2020-04-22 10:40:32,509 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.3}
872 | 2020-04-22 10:41:18,550 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.480574237335205, best pos: [0.0941061 0.21884682 0.27534225 0.7782797 0.95238847 0.08855425
873 | 0.65252266 0.94600766 0.94849138 0.84880172]
874 | 2020-04-22 10:41:18,565 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.5}
875 | 2020-04-22 10:42:04,154 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 21.450718483236653, best pos: [0.2172666 0.04419783 0.88016424 0.78557355 0.72797816 0.3209491
876 | 0.97817398 0.95652832 0.74347454 0.3090275 ]
877 | 2020-04-22 10:42:04,168 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.7}
878 | 2020-04-22 10:42:56,055 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.2}
879 | 2020-04-22 10:43:44,623 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.77403754846512, best pos: [0.41163101 0.11675759 0.70919506 0.8309082 0.76521694 0.17164523
880 | 0.77923149 0.47346738 0.93712181 0.83193576]
881 | 2020-04-22 10:43:44,632 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.3}
882 | 2020-04-22 10:44:21,033 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.601905761261165, best pos: [0.55519305 0.18461039 0.74467526 0.93035929 0.85370196 0.13684539
883 | 0.08591454 0.63036597 0.96948333 0.70443872]
884 | 2020-04-22 10:44:21,046 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.4}
885 | 2020-04-22 10:44:57,756 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.362610448721615, best pos: [0.42668911 0.08458441 0.56133116 0.7251485 0.76956372 0.37232598
886 | 0.99763714 0.70703513 0.61989486 0.55173061]
887 | 2020-04-22 10:44:57,766 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.5}
888 | 2020-04-22 10:45:34,596 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.182388791547165, best pos: [0.6411388 0.09388768 0.97277894 0.96447773 0.70728692 0.23616693
889 | 0.6197131 0.31293621 0.63191456 0.89602622]
890 | 2020-04-22 10:45:34,606 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.7}
891 | 2020-04-22 10:46:11,497 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 21.306376460921022, best pos: [0.69624772 0.05132619 0.21105422 0.84354613 0.51631512 0.49446795
892 | 0.49654293 0.96988236 0.30838553 0.23738737]
893 | 2020-04-22 10:46:11,507 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 0.5, 'w': 0.9}
894 | 2020-04-22 10:46:48,448 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.038461098174196, best pos: [0.6327866 0.1099149 0.40522242 0.70809879 0.42808989 0.34126684
895 | 0.95147008 0.81998759 0.10867348 0.41997012]
896 | 2020-04-22 10:46:48,458 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1, 'w': 0.2}
897 | 2020-04-22 10:47:25,368 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.030967323246045, best pos: [0.67782293 0.13820454 0.69254189 0.54546287 0.96264891 0.21787646
898 | 0.69990374 0.84670523 0.56748765 0.77284541]
899 | 2020-04-22 10:47:25,377 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1, 'w': 0.3}
900 | 2020-04-22 10:48:02,136 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.939058338520173, best pos: [0.3153631 0.2293296 0.81767135 0.45924757 0.54190923 0.09737915
901 | 0.76921871 0.80342892 0.89438873 0.91334685]
902 | 2020-04-22 10:48:02,145 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1, 'w': 0.4}
903 | 2020-04-22 10:48:38,876 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.77167519314931, best pos: [0.93629536 0.14015763 0.92705147 0.6710162 0.66485021 0.23743756
904 | 0.72761131 0.3809678 0.93782214 0.84472924]
905 | 2020-04-22 10:48:38,886 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1, 'w': 0.5}
906 | 2020-04-22 10:49:16,171 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.4907445574029, best pos: [0.72413807 0.16848789 0.93019943 0.32711989 0.29961732 0.18525806
907 | 0.78378782 0.69648292 0.34846162 0.46134667]
908 | 2020-04-22 10:49:16,178 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1, 'w': 0.7}
909 | 2020-04-22 10:49:52,793 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.03064210996244, best pos: [0.71477048 0.14556182 0.78971144 0.53238631 0.90605275 0.16662255
910 | 0.9686285 0.77240101 0.35778879 0.01168789]
911 | 2020-04-22 10:49:52,801 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1, 'w': 0.9}
912 | 2020-04-22 10:50:29,642 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.916053706101735, best pos: [0.50702577 0.08958314 0.94727043 0.92320187 0.4344414 0.25881377
913 | 0.46547644 0.55983387 0.6721173 0.86017975]
914 | 2020-04-22 10:50:29,652 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1.5, 'w': 0.2}
915 | 2020-04-22 10:51:06,591 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.335234740267204, best pos: [0.54620277 0.10900107 0.42675185 0.93459453 0.43112477 0.277387
916 | 0.99179579 0.87613742 0.11283048 0.22451427]
917 | 2020-04-22 10:51:06,600 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1.5, 'w': 0.3}
918 | 2020-04-22 10:51:43,526 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.852678981226163, best pos: [0.86367143 0.10562768 0.61237204 0.4249133 0.83285209 0.28543221
919 | 0.89298478 0.49920997 0.81828635 0.54483278]
920 | 2020-04-22 10:51:43,535 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1.5, 'w': 0.4}
921 | 2020-04-22 10:52:20,235 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.595882065512075, best pos: [0.27341967 0.1680473 0.92955547 0.75003621 0.99417359 0.14671569
922 | 0.72136073 0.28922533 0.8954221 0.56247574]
923 | 2020-04-22 10:52:20,243 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1.5, 'w': 0.5}
924 | 2020-04-22 10:52:57,263 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 21.842978250042027, best pos: [0.13006162 0.21308583 0.48010255 0.78761569 0.29065748 0.22074517
925 | 0.14192661 0.87112842 0.06168956 0.63507353]
926 | 2020-04-22 10:52:57,270 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1.5, 'w': 0.7}
927 | 2020-04-22 10:53:34,029 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.736178547777886, best pos: [0.67630799 0.04875688 0.96235414 0.81823744 0.94268184 0.28405509
928 | 0.33671303 0.43529852 0.86978723 0.30498603]
929 | 2020-04-22 10:53:34,037 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 1.5, 'w': 0.9}
930 | 2020-04-22 10:54:10,474 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.68776230136149, best pos: [0.78138559 0.00607933 0.0771085 0.81853263 0.41761229 0.53898905
931 | 0.70722685 0.3567817 0.49043102 0.27672934]
932 | 2020-04-22 10:54:10,484 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 2, 'w': 0.2}
933 | 2020-04-22 10:54:46,960 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 21.102287549078778, best pos: [0.12300793 0.21262068 0.98055335 0.64344011 0.47748548 0.15788469
934 | 0.56318677 0.60309265 0.23611392 0.47907015]
935 | 2020-04-22 10:54:46,967 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 2, 'w': 0.3}
936 | 2020-04-22 10:55:23,746 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.430843570011127, best pos: [0.8257325 0.18784916 0.24970113 0.92593496 0.73801748 0.07492589
937 | 0.63229907 0.75613605 0.49135978 0.20624219]
938 | 2020-04-22 10:55:23,756 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 2, 'w': 0.4}
939 | 2020-04-22 10:56:00,192 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.798796017366396, best pos: [0.54998562 0.13168851 0.49837895 0.88909822 0.81932227 0.10350667
940 | 0.21113099 0.81838466 0.92065209 0.4357193 ]
941 | 2020-04-22 10:56:00,201 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 2, 'w': 0.5}
942 | 2020-04-22 10:56:36,717 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.33953388646622, best pos: [0.77225628 0.15030921 0.19614311 0.02048012 0.78810122 0.16592771
943 | 0.94219548 0.26284626 0.90297208 0.35168898]
944 | 2020-04-22 10:56:36,726 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 2, 'w': 0.7}
945 | 2020-04-22 10:57:13,516 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 21.243439959929727, best pos: [0.79369272 0.01483602 0.8022804 0.80509522 0.62411548 0.28903114
946 | 0.51017818 0.92000472 0.05244398 0.43839871]
947 | 2020-04-22 10:57:13,525 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 0.5, 'c2': 2, 'w': 0.9}
948 | 2020-04-22 10:57:50,178 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.570495774057594, best pos: [0.25443951 0.19611564 0.41100551 0.87432879 0.5731874 0.1809524
949 | 0.66968371 0.02975039 0.70183628 0.62940798]
950 | 2020-04-22 10:57:50,186 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 0.5, 'w': 0.2}
951 | 2020-04-22 10:58:26,510 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.784479575648586, best pos: [0.64398002 0.12855953 0.78689274 0.85742525 0.3578224 0.26691505
952 | 0.43934298 0.61641826 0.81579593 0.74078662]
953 | 2020-04-22 10:58:26,519 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 0.5, 'w': 0.3}
954 | 2020-04-22 10:59:03,231 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.211455940009525, best pos: [0.61438198 0.08485345 0.38190072 0.76697007 0.7280988 0.37209612
955 | 0.54381172 0.3297414 0.88258069 0.62079305]
956 | 2020-04-22 10:59:03,241 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 0.5, 'w': 0.4}
957 | 2020-04-22 10:59:39,690 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 21.42727051419636, best pos: [0.57236408 0.16281529 0.04843707 0.84556409 0.21696124 0.17502294
958 | 0.32760657 0.50620716 0.40537044 0.92717553]
959 | 2020-04-22 10:59:39,700 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 0.5, 'w': 0.5}
960 | 2020-04-22 11:00:16,290 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.967096034492357, best pos: [0.45150524 0.15114274 0.73685788 0.87471037 0.91111919 0.14947809
961 | 0.65089784 0.95820801 0.78307682 0.8183324 ]
962 | 2020-04-22 11:00:16,299 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 0.5, 'w': 0.7}
963 | 2020-04-22 11:00:53,378 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.798347263987242, best pos: [0.74326357 0.10003942 0.73298394 0.98107992 0.75897514 0.28120956
964 | 0.85849224 0.21413925 0.16924842 0.86898291]
965 | 2020-04-22 11:00:53,386 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 0.5, 'w': 0.9}
966 | 2020-04-22 11:01:30,074 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.375533982304415, best pos: [0.38442502 0.09981542 0.90519314 0.48080667 0.23236224 0.31859279
967 | 0.82021783 0.84262333 0.95115026 0.85158339]
968 | 2020-04-22 11:01:30,082 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1, 'w': 0.2}
969 | 2020-04-22 11:02:06,526 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 16.837162467955796, best pos: [0.43265287 0.11168223 0.845336 0.95803025 0.59360562 0.28932201
970 | 0.9810991 0.82246833 0.8885851 0.57474507]
971 | 2020-04-22 11:02:06,534 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1, 'w': 0.3}
972 | 2020-04-22 11:02:43,640 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.220152445031374, best pos: [0.86251048 0.06946464 0.86311742 0.61096876 0.32158566 0.30536541
973 | 0.7998714 0.73251523 0.59698282 0.0382945 ]
974 | 2020-04-22 11:02:43,649 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1, 'w': 0.4}
975 | 2020-04-22 11:03:20,639 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.817369367813473, best pos: [0.77592321 0.06129621 0.22655521 0.8868439 0.27060121 0.36619933
976 | 0.14881041 0.30639376 0.8523708 0.84079005]
977 | 2020-04-22 11:03:20,648 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1, 'w': 0.5}
978 | 2020-04-22 11:03:57,443 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.525068133804936, best pos: [0.14260145 0.15251053 0.3507461 0.90677136 0.62666827 0.2650425
979 | 0.55952681 0.05027349 0.33454708 0.90070243]
980 | 2020-04-22 11:03:57,451 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1, 'w': 0.7}
981 | 2020-04-22 11:04:33,921 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 21.55531057532279, best pos: [0.66848029 0.06742026 0.5517784 0.92857367 0.12443129 0.52355225
982 | 0.66238139 0.72826909 0.21843731 0.029953 ]
983 | 2020-04-22 11:04:33,930 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1, 'w': 0.9}
984 | 2020-04-22 11:05:10,725 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.639693309853087, best pos: [0.57771762 0.18968281 0.1591864 0.88452281 0.7916599 0.05016781
985 | 0.54040748 0.97656698 0.80810006 0.49225737]
986 | 2020-04-22 11:05:10,737 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1.5, 'w': 0.2}
987 | 2020-04-22 11:05:47,417 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.092707412701273, best pos: [0.28747017 0.18365406 0.59813255 0.90384529 0.91667005 0.13983761
988 | 0.11951973 0.65261115 0.89873194 0.932686 ]
989 | 2020-04-22 11:05:47,426 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1.5, 'w': 0.3}
990 | 2020-04-22 11:06:23,859 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.83793131102401, best pos: [0.59020339 0.13777918 0.72872108 0.5343848 0.82145413 0.25323226
991 | 0.53535627 0.02022857 0.69712397 0.79340118]
992 | 2020-04-22 11:06:23,868 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1.5, 'w': 0.4}
993 | 2020-04-22 11:07:00,894 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.842222930439693, best pos: [0.21619473 0.15536714 0.91053569 0.59034151 0.92576408 0.07140816
994 | 0.75600383 0.87819457 0.94899628 0.03359529]
995 | 2020-04-22 11:07:00,903 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1.5, 'w': 0.5}
996 | 2020-04-22 11:07:37,843 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.661138641047117, best pos: [0.6438981 0.01406438 0.72724305 0.97005653 0.88100648 0.46002981
997 | 0.78465913 0.37365841 0.95000377 0.05041782]
998 | 2020-04-22 11:07:37,852 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1.5, 'w': 0.7}
999 | 2020-04-22 11:08:14,402 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.11854189575923, best pos: [0.10864348 0.09971069 0.72460126 0.82664423 0.48766828 0.41236302
1000 | 0.74654533 0.98381975 0.98056327 0.28584711]
1001 | 2020-04-22 11:08:14,412 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1.5, 'w': 0.9}
1002 | 2020-04-22 11:08:51,242 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.81897957476335, best pos: [0.35342009 0.21709188 0.76058274 0.96734961 0.96767958 0.11811175
1003 | 0.13096748 0.39271608 0.56455373 0.55455476]
1004 | 2020-04-22 11:08:51,251 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 2, 'w': 0.2}
1005 | 2020-04-22 11:09:28,138 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.97023597790132, best pos: [0.73244308 0.04816952 0.8397546 0.25343373 0.40921257 0.47527471
1006 | 0.851203 0.42963469 0.01915089 0.76512285]
1007 | 2020-04-22 11:09:28,146 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 2, 'w': 0.3}
1008 | 2020-04-22 11:10:06,397 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.823674175191506, best pos: [0.45777536 0.14145572 0.98852428 0.99393894 0.72807821 0.19493818
1009 | 0.68821178 0.49808578 0.59984358 0.30328856]
1010 | 2020-04-22 11:10:06,405 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 2, 'w': 0.4}
1011 | 2020-04-22 11:10:43,131 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.41882037832371, best pos: [0.5858691 0.02565902 0.56927551 0.79607896 0.62761431 0.43200151
1012 | 0.95932151 0.49778319 0.92338309 0.9860986 ]
1013 | 2020-04-22 11:10:43,139 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 2, 'w': 0.5}
1014 | 2020-04-22 11:11:20,021 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.33509318285436, best pos: [0.28090861 0.17787243 0.66758597 0.69409531 0.75672569 0.12751568
1015 | 0.37465464 0.6832174 0.74547716 0.64538458]
1016 | 2020-04-22 11:11:20,030 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 2, 'w': 0.7}
1017 | 2020-04-22 11:11:56,774 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.898543461383625, best pos: [0.91814651 0.19611011 0.639131 0.37890365 0.68313503 0.03024902
1018 | 0.81717812 0.42772557 0.97101096 0.52079528]
1019 | 2020-04-22 11:11:56,782 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 2, 'w': 0.9}
1020 | 2020-04-22 11:12:33,431 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.836249021854083, best pos: [0.97296568 0.0979245 0.73080701 0.60233248 0.3967681 0.29923938
1021 | 0.90272223 0.65371932 0.40929628 0.73406115]
1022 | 2020-04-22 11:12:33,440 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 0.5, 'w': 0.2}
1023 | 2020-04-22 11:13:10,305 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.983243374637077, best pos: [0.99593059 0.04429058 0.46758496 0.40925872 0.66293941 0.30483449
1024 | 0.37804713 0.42761627 0.43207091 0.62305129]
1025 | 2020-04-22 11:13:10,315 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 0.5, 'w': 0.3}
1026 | 2020-04-22 11:13:51,189 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 22.173217428268337, best pos: [0.33586538 0.29922191 0.19748766 0.03949117 0.56551088 0.05498477
1027 | 0.99386424 0.71240823 0.89782387 0.90613872]
1028 | 2020-04-22 11:13:51,199 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 0.5, 'w': 0.4}
1029 | 2020-04-22 11:14:31,424 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.8964608460335, best pos: [0.59435365 0.17865013 0.16766775 0.50343223 0.57378285 0.13271814
1030 | 0.8016886 0.45114032 0.82782051 0.92888652]
1031 | 2020-04-22 11:14:31,432 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 0.5, 'w': 0.5}
1032 | 2020-04-22 11:16:25,384 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1, 'w': 0.3}
1033 | 2020-04-22 11:18:13,655 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.904883125124996, best pos: [0.46755491 0.18103788 0.7204586 0.86458675 0.81970516 0.12138651
1034 | 0.57736084 0.9859868 0.09438803 0.28196841]
1035 | 2020-04-22 11:18:13,667 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1, 'w': 0.4}
1036 | 2020-04-22 11:20:01,905 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.930467960207874, best pos: [0.62039251 0.15221364 0.67316598 0.98702309 0.58574683 0.2162405
1037 | 0.95006192 0.40632179 0.46067507 0.95006906]
1038 | 2020-04-22 11:20:01,913 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1, 'w': 0.5}
1039 | 2020-04-22 11:21:48,187 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.574111023715854, best pos: [0.85463535 0.13023615 0.84678295 0.45632237 0.51866 0.20303744
1040 | 0.90209245 0.77322573 0.02012585 0.51420021]
1041 | 2020-04-22 11:21:48,197 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1.5, 'w': 0.3}
1042 | 2020-04-22 11:23:37,858 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.542088769759385, best pos: [0.84074727 0.06739436 0.12493154 0.42706237 0.44405972 0.41922902
1043 | 0.86498616 0.69272353 0.96262616 0.72084371]
1044 | 2020-04-22 11:23:37,868 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1.5, 'w': 0.4}
1045 | 2020-04-22 11:25:24,319 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.672472988008863, best pos: [0.89257976 0.11941204 0.7666638 0.45234696 0.89973985 0.18444722
1046 | 0.75652263 0.88277501 0.5040361 0.44858418]
1047 | 2020-04-22 11:25:24,329 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 1.5, 'w': 0.5}
1048 | 2020-04-22 11:27:12,062 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.223294417904054, best pos: [0.50603749 0.14578305 0.2169216 0.21076752 0.95765601 0.11224901
1049 | 0.87013466 0.96333897 0.7851961 0.63501562]
1050 | 2020-04-22 11:27:12,071 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 2, 'w': 0.3}
1051 | 2020-04-22 11:28:58,748 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.06784790382278, best pos: [0.70186199 0.12433435 0.94356783 0.8205113 0.67061262 0.144296
1052 | 0.76639892 0.45161055 0.2285411 0.45808623]
1053 | 2020-04-22 11:28:58,758 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 2, 'w': 0.4}
1054 | 2020-04-22 11:30:45,204 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.026069193159337, best pos: [0.86522305 0.04845101 0.53142367 0.69853203 0.90253476 0.34502217
1055 | 0.93907859 0.39064748 0.53764279 0.45058781]
1056 | 2020-04-22 11:30:45,214 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1, 'c2': 2, 'w': 0.5}
1057 | 2020-04-22 11:32:34,079 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.67446710799242, best pos: [0.75465465 0.09088344 0.77200044 0.96068453 0.96384165 0.23619716
1058 | 0.74314027 0.19008715 0.77571904 0.44092902]
1059 | 2020-04-22 11:32:34,088 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1, 'w': 0.3}
1060 | 2020-04-22 11:34:32,079 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.069543201934014, best pos: [0.88597818 0.00853409 0.74031218 0.88875565 0.54941213 0.43925541
1061 | 0.9230603 0.69440488 0.82878371 0.70260888]
1062 | 2020-04-22 11:34:32,090 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1, 'w': 0.4}
1063 | 2020-04-22 11:36:36,065 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.96851742657791, best pos: [0.57499537 0.15746784 0.57908108 0.51394616 0.81804645 0.07171127
1064 | 0.9275484 0.89646642 0.44875985 0.97153059]
1065 | 2020-04-22 11:36:36,075 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1, 'w': 0.5}
1066 | 2020-04-22 11:39:17,895 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.2869778977049, best pos: [0.50803063 0.16003327 0.19502577 0.93468747 0.87142547 0.13462094
1067 | 0.86031448 0.92084055 0.54072917 0.67203553]
1068 | 2020-04-22 11:39:17,905 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.3}
1069 | 2020-04-22 11:41:17,216 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.77746832279427, best pos: [0.85599101 0.15972462 0.90329783 0.77573854 0.73670669 0.15461206
1070 | 0.38482807 0.20527119 0.98588992 0.55798821]
1071 | 2020-04-22 11:41:17,227 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.4}
1072 | 2020-04-22 11:43:24,721 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.926864827888263, best pos: [0.45691642 0.12285673 0.55940131 0.74938497 0.59889562 0.38509823
1073 | 0.63060534 0.3290107 0.23375931 0.90675695]
1074 | 2020-04-22 11:43:24,730 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1.5, 'w': 0.5}
1075 | 2020-04-22 11:43:56,093 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1, 'w': 0.4}
1076 | 2020-04-22 11:44:10,271 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1, 'w': 0.4}
1077 | 2020-04-22 11:44:29,754 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.00545077713422, best pos: [0.37354819 0.17350837 0.96925312 0.87112344 0.62864833 0.33768476
1078 | 0.81985705 0.06455628 0.93773769 0.78776849]
1079 | 2020-04-22 11:44:29,763 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1, 'w': 0.5}
1080 | 2020-04-22 11:44:49,852 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.31945889573382, best pos: [0.96909723 0.05289705 0.5619423 0.55932221 0.82174306 0.25345982
1081 | 0.73183574 0.57604961 0.95536731 0.7543834 ]
1082 | 2020-04-22 11:44:49,852 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 2, 'w': 0.4}
1083 | 2020-04-22 11:45:11,202 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 24.863101147168457, best pos: [0.50479288 0.29480336 0.55928481 0.10657959 0.38808907 0.15941605
1084 | 0.7384265 0.32346024 0.17802774 0.76149304]
1085 | 2020-04-22 11:45:11,208 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 2, 'w': 0.5}
1086 | 2020-04-22 11:45:30,084 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 20.630305736263676, best pos: [0.61188693 0.19510543 0.97110174 0.25745521 0.99815126 0.10199959
1087 | 0.74356074 0.88852032 0.8792083 0.04891527]
1088 | 2020-04-22 11:45:30,091 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 2.5, 'c2': 1, 'w': 0.4}
1089 | 2020-04-22 11:45:49,128 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.266357850328752, best pos: [0.71066977 0.01950631 0.94621377 0.76292385 0.91392819 0.39516455
1090 | 0.89833904 0.84111262 0.98842884 0.51015758]
1091 | 2020-04-22 11:45:49,136 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 2.5, 'c2': 1, 'w': 0.5}
1092 | 2020-04-22 11:46:07,468 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.699751158260142, best pos: [0.89790477 0.15348261 0.81096286 0.48662435 0.85397745 0.00423294
1093 | 0.41146576 0.94906948 0.6739679 0.80452654]
1094 | 2020-04-22 11:46:07,476 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 2.5, 'c2': 2, 'w': 0.4}
1095 | 2020-04-22 11:46:26,449 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 22.885035224958795, best pos: [0.28659498 0.27946051 0.49214782 0.72307283 0.19405505 0.05352225
1096 | 0.69575675 0.8612681 0.66138287 0.81883459]
1097 | 2020-04-22 11:46:26,457 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 2.5, 'c2': 2, 'w': 0.5}
1098 | 2020-04-22 11:53:06,513 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 3.6920413954374425, 'c2': 7.430188212537599, 'w': 4.8195029313390645, 'k': 13, 'p': 1}
1099 | 2020-04-22 11:53:43,082 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.91018493881879, best pos: [0.59883457 0.1512672 0.945623 0.8672119 0.75649107 0.04705654
1100 | 0.21400018 0.89996936 0.30761946 0.95504892]
1101 | 2020-04-22 11:53:43,091 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 4.09839772727009, 'c2': 9.464883999020937, 'w': 3.195865752030682, 'k': 12, 'p': 1}
1102 | 2020-04-22 11:54:19,387 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.990188881961696, best pos: [0.10350925 0.16548549 0.9991324 0.66578297 0.76805853 0.19653128
1103 | 0.98514763 0.7606224 0.66079691 0.83442749]
1104 | 2020-04-22 11:54:19,397 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.170092641710049, 'c2': 7.780725553906665, 'w': 4.066098681304122, 'k': 14, 'p': 1}
1105 | 2020-04-22 11:55:00,042 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.835291577035232, best pos: [0.93666839 0.00983273 0.59944075 0.6519685 0.51042589 0.4204421
1106 | 0.33973289 0.88619053 0.41205006 0.96491921]
1107 | 2020-04-22 11:55:00,054 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 4.540899749009803, 'c2': 9.986241449892937, 'w': 4.234443196815232, 'k': 12, 'p': 1}
1108 | 2020-04-22 11:55:39,973 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1, 'w': 0.4}
1109 | 2020-04-22 11:56:21,429 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 17.94486689031527, best pos: [0.96589168 0.04596537 0.95171847 0.83362031 0.76907974 0.33113423
1110 | 0.75828914 0.11698337 0.96285252 0.99232822]
1111 | 2020-04-22 11:56:21,439 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1, 'w': 0.5}
1112 | 2020-04-22 11:57:01,910 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.751080384124386, best pos: [0.48642725 0.24462979 0.76132127 0.94347795 0.73094072 0.00927042
1113 | 0.69077797 0.82475106 0.01464146 0.96679889]
1114 | 2020-04-22 11:57:01,921 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 2, 'w': 0.4}
1115 | 2020-04-22 11:57:45,726 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.92961694179836, best pos: [0.4682598 0.19147849 0.17264067 0.742554 0.37118747 0.29119708
1116 | 0.95815969 0.20353067 0.39982697 0.97416724]
1117 | 2020-04-22 11:57:45,732 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 2, 'w': 0.5}
1118 | 2020-04-22 11:58:24,765 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.13924547762402, best pos: [0.6823081 0.02303993 0.14475241 0.87780873 0.81858771 0.36741447
1119 | 0.98719073 0.74455669 0.0966704 0.50676828]
1120 | 2020-04-22 11:58:24,772 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 2.5, 'c2': 1, 'w': 0.4}
1121 | 2020-04-22 11:59:03,212 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.477761110758156, best pos: [0.89499027 0.0287801 0.51601253 0.69433202 0.43393138 0.33635162
1122 | 0.54277203 0.98741568 0.79075541 0.34341068]
1123 | 2020-04-22 11:59:03,222 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 2.5, 'c2': 1, 'w': 0.5}
1124 | 2020-04-22 11:59:50,478 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.215223619053564, best pos: [0.46744453 0.20376641 0.37911156 0.90185171 0.49058645 0.10231002
1125 | 0.59639012 0.92429464 0.4265393 0.98983952]
1126 | 2020-04-22 11:59:50,478 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 2.5, 'c2': 2, 'w': 0.4}
1127 | 2020-04-22 12:00:31,582 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 19.95600146461645, best pos: [0.20903311 0.12142491 0.90421026 0.79732814 0.45749549 0.43197991
1128 | 0.83207409 0.70494342 0.13878596 0.7322814 ]
1129 | 2020-04-22 12:00:31,587 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 2.5, 'c2': 2, 'w': 0.5}
1130 | 2020-04-22 12:01:11,702 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 18.4261119064102, best pos: [0.39356566 0.16747514 0.30628814 0.83675966 0.86088962 0.11319215
1131 | 0.49902368 0.89946685 0.64402835 0.50165155]
1132 | 2020-04-22 12:07:59,933 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1, 'w': 0.4}
1133 | 2020-04-22 12:08:14,239 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 1.5, 'c2': 1, 'w': 0.4}
1134 | 2020-04-22 12:12:29,269 - pyswarms.single.global_best - INFO - Optimize for 1000 iters with {'c1': 1.5, 'c2': 1, 'w': 0.4}
1135 | 2020-04-22 12:12:54,475 - pyswarms.single.global_best - INFO - Optimize for 300 iters with {'c1': 1.5, 'c2': 1, 'w': 0.4}
1136 | 2020-04-22 12:13:14,130 - pyswarms.single.global_best - INFO - Optimize for 200 iters with {'c1': 1.5, 'c2': 1, 'w': 0.4}
1137 | 2020-04-22 12:14:45,353 - pyswarms.single.global_best - INFO - Optimize for 100 iters with {'c1': 1.5, 'c2': 1, 'w': 0.4}
1138 | 2020-04-22 12:21:37,357 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 15.164097054021568, best pos: [0.57581349 0.07162225 0.98140094 0.99585446 0.90376032 0.22333555
1139 | 0.98617389 0.98324971 0.9978622 0.98066646]
1140 |
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | alabaster==0.7.12
2 | anaconda-client==1.7.2
3 | anaconda-navigator==1.9.12
4 | anaconda-project==0.8.3
5 | argh==0.26.2
6 | asn1crypto==1.3.0
7 | astroid==2.3.3
8 | astropy==4.0
9 | atomicwrites==1.3.0
10 | attrs==19.3.0
11 | autopep8==1.4.4
12 | Babel==2.8.0
13 | backcall==0.1.0
14 | backports.functools-lru-cache==1.6.1
15 | backports.shutil-get-terminal-size==1.0.0
16 | backports.tempfile==1.0
17 | backports.weakref==1.0.post1
18 | bcrypt==3.1.7
19 | beautifulsoup4==4.8.2
20 | bitarray==1.2.1
21 | bkcharts==0.2
22 | bleach==3.1.0
23 | bokeh==1.4.0
24 | boto==2.49.0
25 | Bottleneck==1.3.2
26 | certifi==2019.11.28
27 | cffi==1.14.0
28 | chardet==3.0.4
29 | Click==7.0
30 | cloudpickle==1.3.0
31 | clyent==1.2.2
32 | colorama==0.4.3
33 | comtypes==1.1.7
34 | conda==4.8.2
35 | conda-build==3.18.11
36 | conda-package-handling==1.6.0
37 | conda-verify==3.4.2
38 | contextlib2==0.6.0.post1
39 | cryptography==2.8
40 | cycler==0.10.0
41 | Cython==0.29.15
42 | cytoolz==0.10.1
43 | dask==2.11.0
44 | decorator==4.4.1
45 | defusedxml==0.6.0
46 | diff-match-patch==20181111
47 | distributed==2.11.0
48 | docutils==0.16
49 | entrypoints==0.3
50 | et-xmlfile==1.0.1
51 | fastcache==1.1.0
52 | filelock==3.0.12
53 | flake8==3.7.9
54 | Flask==1.1.1
55 | fsspec==0.6.2
56 | future==0.18.2
57 | gevent==1.4.0
58 | glob2==0.7
59 | greenlet==0.4.15
60 | h5py==2.10.0
61 | HeapDict==1.0.1
62 | html5lib==1.0.1
63 | hypothesis==5.5.4
64 | idna==2.8
65 | imageio==2.6.1
66 | imagesize==1.2.0
67 | importlib-metadata==1.5.0
68 | intervaltree==3.0.2
69 | ipykernel==5.1.4
70 | ipython==7.12.0
71 | ipython-genutils==0.2.0
72 | ipywidgets==7.5.1
73 | isort==4.3.21
74 | itsdangerous==1.1.0
75 | jdcal==1.4.1
76 | jedi==0.14.1
77 | Jinja2==2.11.1
78 | joblib==0.14.1
79 | json5==0.9.1
80 | jsonschema==3.2.0
81 | jupyter==1.0.0
82 | jupyter-client==5.3.4
83 | jupyter-console==6.1.0
84 | jupyter-core==4.6.1
85 | jupyterlab==1.2.6
86 | jupyterlab-server==1.0.6
87 | keyring==21.1.0
88 | kiwisolver==1.1.0
89 | lazy-object-proxy==1.4.3
90 | libarchive-c==2.8
91 | llvmlite==0.31.0
92 | locket==0.2.0
93 | lxml==4.5.0
94 | MarkupSafe==1.1.1
95 | matplotlib==3.1.3
96 | mccabe==0.6.1
97 | menuinst==1.4.16
98 | mistune==0.8.4
99 | mkl-fft==1.0.15
100 | mkl-random==1.1.0
101 | mkl-service==2.3.0
102 | mock==4.0.1
103 | more-itertools==8.2.0
104 | mpmath==1.1.0
105 | msgpack==0.6.1
106 | multipledispatch==0.6.0
107 | navigator-updater==0.2.1
108 | nbconvert==5.6.1
109 | nbformat==5.0.4
110 | networkx==2.4
111 | nltk==3.4.5
112 | nose==1.3.7
113 | notebook==6.0.3
114 | numba==0.48.0
115 | numexpr==2.7.1
116 | numpy==1.18.1
117 | numpydoc==0.9.2
118 | olefile==0.46
119 | openpyxl==3.0.3
120 | packaging==20.1
121 | pandas==1.0.1
122 | pandocfilters==1.4.2
123 | paramiko==2.7.1
124 | parso==0.5.2
125 | partd==1.1.0
126 | path==13.1.0
127 | pathlib2==2.3.5
128 | pathtools==0.1.2
129 | patsy==0.5.1
130 | pep8==1.7.1
131 | pexpect==4.8.0
132 | pickleshare==0.7.5
133 | Pillow==7.0.0
134 | pkginfo==1.5.0.1
135 | pluggy==0.13.1
136 | ply==3.11
137 | prometheus-client==0.7.1
138 | prompt-toolkit==3.0.3
139 | psutil==5.6.7
140 | py==1.8.1
141 | pycodestyle==2.5.0
142 | pycosat==0.6.3
143 | pycparser==2.19
144 | pycrypto==2.6.1
145 | pycurl==7.43.0.5
146 | pydocstyle==4.0.1
147 | pyflakes==2.1.1
148 | Pygments==2.5.2
149 | pylint==2.4.4
150 | PyNaCl==1.3.0
151 | pyodbc===4.0.0-unsupported
152 | pyOpenSSL==19.1.0
153 | pyparsing==2.4.6
154 | pyreadline==2.1
155 | pyrsistent==0.15.7
156 | PySocks==1.7.1
157 | pytest==5.3.5
158 | pytest-arraydiff==0.3
159 | pytest-astropy==0.8.0
160 | pytest-astropy-header==0.1.2
161 | pytest-doctestplus==0.5.0
162 | pytest-openfiles==0.4.0
163 | pytest-remotedata==0.3.2
164 | python-dateutil==2.8.1
165 | python-jsonrpc-server==0.3.4
166 | python-language-server==0.31.7
167 | pytz==2019.3
168 | PyWavelets==1.1.1
169 | pywin32==227
170 | pywin32-ctypes==0.2.0
171 | pywinpty==0.5.7
172 | PyYAML==5.3
173 | pyzmq==18.1.1
174 | QDarkStyle==2.8
175 | QtAwesome==0.6.1
176 | qtconsole==4.6.0
177 | QtPy==1.9.0
178 | requests==2.22.0
179 | rope==0.16.0
180 | Rtree==0.9.3
181 | ruamel-yaml==0.15.87
182 | scikit-image==0.16.2
183 | scikit-learn==0.22.1
184 | scipy==1.4.1
185 | seaborn==0.10.0
186 | Send2Trash==1.5.0
187 | simplegeneric==0.8.1
188 | singledispatch==3.4.0.3
189 | six==1.14.0
190 | snowballstemmer==2.0.0
191 | sortedcollections==1.1.2
192 | sortedcontainers==2.1.0
193 | soupsieve==1.9.5
194 | Sphinx==2.4.0
195 | sphinxcontrib-applehelp==1.0.1
196 | sphinxcontrib-devhelp==1.0.1
197 | sphinxcontrib-htmlhelp==1.0.2
198 | sphinxcontrib-jsmath==1.0.1
199 | sphinxcontrib-qthelp==1.0.2
200 | sphinxcontrib-serializinghtml==1.1.3
201 | sphinxcontrib-websupport==1.2.0
202 | spyder==4.0.1
203 | spyder-kernels==1.8.1
204 | SQLAlchemy==1.3.13
205 | statsmodels==0.11.0
206 | sympy==1.5.1
207 | tables==3.6.1
208 | tblib==1.6.0
209 | terminado==0.8.3
210 | testpath==0.4.4
211 | toolz==0.10.0
212 | tornado==6.0.3
213 | tqdm==4.42.1
214 | traitlets==4.3.3
215 | ujson==1.35
216 | unicodecsv==0.14.1
217 | urllib3==1.25.8
218 | watchdog==0.10.2
219 | wcwidth==0.1.8
220 | webencodings==0.5.1
221 | Werkzeug==1.0.0
222 | widgetsnbextension==3.5.1
223 | win-inet-pton==1.1.0
224 | win-unicode-console==0.5
225 | wincertstore==0.2
226 | wrapt==1.11.2
227 | xlrd==1.2.0
228 | XlsxWriter==1.2.7
229 | xlwings==0.17.1
230 | xlwt==1.3.0
231 | xmltodict==0.12.0
232 | yapf==0.28.0
233 | zict==1.0.0
234 | zipp==2.2.0
235 |
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/toggle.tpl:
--------------------------------------------------------------------------------
1 | {%- extends 'full.tpl' -%}
2 |
3 | {% block output_group %}
4 |
5 | {{ super() }}
6 |
7 | {% endblock output_group %}
8 |
9 | {% block input_group -%}
10 |
11 | {{ super() }}
12 |
13 | {% endblock input_group %}
14 |
15 | {%- block input -%}
16 |
17 |
18 | {{ super() }}
19 |
20 |
21 |
22 |