├── .gitattributes
└── COA
    ├── initialization.m
    ├── license.txt
    ├── mainCOA.m
    ├── func_plot.m
    ├── COA.m
    └── Get_F.m


/.gitattributes:
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1 | # Auto detect text files and perform LF normalization
2 | * text=auto
3 | 


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/COA/initialization.m:
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 1 | 
 2 | function X=initialization(N,Dim,UB,LB)
 3 | 
 4 | B_no= size(UB,2); % numnber of boundaries
 5 | 
 6 | if B_no==1
 7 |     X=rand(N,Dim).*(UB-LB)+LB;
 8 | end
 9 | 
10 | % If each variable has a different lb and ub
11 | if B_no>1
12 |     for i=1:Dim
13 |         Ub_i=UB(i);
14 |         Lb_i=LB(i);
15 |         X(:,i)=rand(N,1).*(Ub_i-Lb_i)+Lb_i;
16 |     end
17 |     end


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/COA/license.txt:
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 1 | Copyright (c) 2023, Heming Jia,Raohonghua,Wen changsheng,Seyedali Mirjalili
 2 | All rights reserved.
 3 | 
 4 | Redistribution and use in source and binary forms, with or without
 5 | modification, are permitted provided that the following conditions are met:
 6 | 
 7 | * Redistributions of source code must retain the above copyright notice, this
 8 |   list of conditions and the following disclaimer.
 9 | 
10 | * Redistributions in binary form must reproduce the above copyright notice,
11 |   this list of conditions and the following disclaimer in the documentation
12 |   and/or other materials provided with the distribution
13 | 
14 | * Neither the name of contributor's University nor the names of its
15 |   contributors may be used to endorse or promote products derived from this
16 |   software without specific prior written permission.
17 | 
18 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
19 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
20 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
21 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
22 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
23 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
24 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
25 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
26 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
27 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
28 | 


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/COA/mainCOA.m:
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 1 | %  Crayfish Optimization Algorithm(COA)
 2 | %
 3 | %  Source codes demo version 1.0                                                                      
 4 | %                                                                                                     
 5 | %  The 11th Gen Intel(R) Core(TM) i7-11700 processor with the primary frequency of 2.50GHz, 16GB memory, and the operating system of 64-bit windows 11 using matlab2021a.                                                                
 6 | %                                                                                                     
 7 | %  Author and programmer: Heming Jia,Honghua Rao,Changsheng Wen,Seyedali Mirjalili                                                                          
 8 | %         e-Mail: jiaheminglucky99@126.com;rao12138@163.com                                                                                                                                                                                                                                                
 9 | 
10 | 
11 | clear all 
12 | close all
13 | clc
14 | 
15 | N=100;  %Number of search agents
16 | F_name='F3';     %Name of the test function
17 | T=200;           %Maximum number of iterations
18 | 
19 |     
20 | [lb,ub,dim,fobj]=Get_F(F_name); %Get details of the benchmark functions
21 | [best_fun,best_position,cuve_f,global_Cov]=COA(N,T,lb,ub,dim,fobj); 
22 | 
23 | 
24 | figure('Position',[454   445   694   297]);
25 | subplot(1,3,1);
26 | func_plot(F_name);     % Function plot
27 | title('Parameter space')
28 | xlabel('x_1');
29 | ylabel('x_2');
30 | zlabel([F_name,'( x_1 , x_2 )'])
31 | subplot(1,3,2);       % Convergence plot
32 | semilogy(cuve_f,'LineWidth',3)
33 | subplot(1,3,3);       % Convergence plot
34 | semilogy(global_Cov,'LineWidth',3)
35 | xlabel('Iteration#');
36 | ylabel('Best fitness so far');
37 | legend('COA');
38 | 
39 | 
40 | 
41 | display(['The best-obtained solution by COA is : ', num2str(best_position)]);  
42 | display(['The best optimal value of the objective funciton found by COA is : ', num2str(best_fun)]);  


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/COA/func_plot.m:
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 1 | 
 2 | function func_plot(func_name)
 3 | 
 4 | [LB,UB,Dim,F_obj]=Get_F(func_name);
 5 | 
 6 | switch func_name 
 7 |     case 'F1' 
 8 |         x=-100:2:100; y=x; %[-100,100]
 9 |         
10 |     case 'F2' 
11 |         x=-100:2:100; y=x; %[-10,10]
12 |         
13 |     case 'F3' 
14 |         x=-100:2:100; y=x; %[-100,100]
15 |         
16 |     case 'F4' 
17 |         x=-100:2:100; y=x; %[-100,100]
18 |     case 'F5' 
19 |         x=-200:2:200; y=x; %[-5,5]
20 |     case 'F6' 
21 |         x=-100:2:100; y=x; %[-100,100]
22 |     case 'F7' 
23 |         x=-1:0.03:1;  y=x  %[-1,1]
24 |     case 'F8' 
25 |         x=-500:10:500;y=x; %[-500,500]
26 |     case 'F9' 
27 |         x=-5:0.1:5;   y=x; %[-5,5]    
28 |     case 'F10' 
29 |         x=-20:0.5:20; y=x;%[-500,500]
30 |     case 'F11' 
31 |         x=-500:10:500; y=x;%[-0.5,0.5]
32 |     case 'F12' 
33 |         x=-10:0.1:10; y=x;%[-pi,pi]
34 |     case 'F13' 
35 |         x=-5:0.08:5; y=x;%[-3,1]
36 |     case 'F14' 
37 |         x=-100:2:100; y=x;%[-100,100]
38 |     case 'F15' 
39 |         x=-5:0.1:5; y=x;%[-5,5]
40 |     case 'F16' 
41 |         x=-1:0.01:1; y=x;%[-5,5]
42 |     case 'F17' 
43 |         x=-5:0.1:5; y=x;%[-5,5]
44 |     case 'F18' 
45 |         x=-5:0.06:5; y=x;%[-5,5]
46 |     case 'F19' 
47 |         x=-5:0.1:5; y=x;%[-5,5]
48 |     case 'F20' 
49 |         x=-5:0.1:5; y=x;%[-5,5]        
50 |     case 'F21' 
51 |         x=-5:0.1:5; y=x;%[-5,5]
52 |     case 'F22' 
53 |         x=-5:0.1:5; y=x;%[-5,5]     
54 |     case 'F23' 
55 |         x=-5:0.1:5; y=x;%[-5,5]  
56 | end    
57 | 
58 |     
59 | 
60 | L=length(x);
61 | f=[];
62 | 
63 | for i=1:L
64 |     for j=1:L
65 |         if strcmp(func_name,'F15')==0 && strcmp(func_name,'F19')==0 && strcmp(func_name,'F20')==0 && strcmp(func_name,'F21')==0 && strcmp(func_name,'F22')==0 && strcmp(func_name,'F23')==0
66 |             f(i,j)=F_obj([x(i),y(j)]);
67 |         end
68 |         if strcmp(func_name,'F15')==1
69 |             f(i,j)=F_obj([x(i),y(j),0,0]);
70 |         end
71 |         if strcmp(func_name,'F19')==1
72 |             f(i,j)=F_obj([x(i),y(j),0]);
73 |         end
74 |         if strcmp(func_name,'F20')==1
75 |             f(i,j)=F_obj([x(i),y(j),0,0,0,0]);
76 |         end       
77 |         if strcmp(func_name,'F21')==1 || strcmp(func_name,'F22')==1 ||strcmp(func_name,'F23')==1
78 |             f(i,j)=F_obj([x(i),y(j),0,0]);
79 |         end          
80 |     end
81 | end
82 | 
83 | surfc(x,y,f,'LineStyle','none');
84 | 
85 | end
86 | 
87 | 


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/COA/COA.m:
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  1 | %  Crayfish Optimization Algorithm(COA)
  2 | %
  3 | %  Source codes demo version 1.0                                                                      
  4 | %                                                                                                     
  5 | %  The 11th Gen Intel(R) Core(TM) i7-11700 processor with the primary frequency of 2.50GHz, 16GB memory, and the operating system of 64-bit windows 11 using matlab2021a.                                                                
  6 | %                                                                                                     
  7 | %  Author and programmer: Heming Jia,Raohonghua,Wen changsheng,Seyedali Mirjalili                                                                          
  8 | %         e-Mail: jiaheminglucky99@126.com;rao12138@163.com                                                           
  9 | %                                   
 10 | %                                                                                                                                                   
 11 | %_______________________________________________________________________________________________
 12 | % You can simply define your cost function in a seperate file and load its handle to fobj 
 13 | % The initial parameters that you need are:
 14 | %__________________________________________
 15 | % fobj = @YourCostFunction
 16 | % dim = number of your variables
 17 | % T = maximum number of iterations
 18 | % N = number of search agents
 19 | % lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n
 20 | % ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n
 21 | % If all the variables have equal lower bound you can just
 22 | % define lb and ub as two single numbers
 23 | function [best_fun,best_position,cuve_f,global_Cov]  =COA(N,T,lb,ub,dim,fobj)
 24 | %% Define Parameters
 25 | cuve_f=zeros(1,T); 
 26 | X=initialization(N,dim,ub,lb); %Initialize population
 27 | global_Cov = zeros(1,T);
 28 | Best_fitness = inf;
 29 | best_position = zeros(1,dim);
 30 | fitness_f = zeros(1,N);
 31 | for i=1:N
 32 |    fitness_f(i) =  fobj(X(i,:)); %Calculate the fitness value of the function
 33 |    if fitness_f(i)<Best_fitness
 34 |        Best_fitness = fitness_f(i);
 35 |        best_position = X(i,:);
 36 |    end
 37 | end
 38 | global_position = best_position; 
 39 | global_fitness = Best_fitness;
 40 | cuve_f(1)=Best_fitness;
 41 | t=1; 
 42 | while(t<=T)
 43 |     C = 2-(t/T); %Eq.(7)
 44 |     temp = rand*15+20; %Eq.(3)
 45 |     xf = (best_position+global_position)/2; % Eq.(5)
 46 |     Xfood = best_position;
 47 |     for i = 1:N
 48 |         if temp>30
 49 |             %% summer resort stage
 50 |             if rand<0.5
 51 |                 Xnew(i,:) = X(i,:)+C*rand(1,dim).*(xf-X(i,:)); %Eq.(6)
 52 |             else
 53 |             %% competition stage
 54 |                 for j = 1:dim
 55 |                     z = round(rand*(N-1))+1;  %Eq.(9)
 56 |                     Xnew(i,j) = X(i,j)-X(z,j)+xf(j);  %Eq.(8)
 57 |                 end
 58 |             end
 59 |         else
 60 |             %% foraging stage
 61 |             P = 3*rand*fitness_f(i)/fobj(Xfood); %Eq.(4)
 62 |             if P>2   % The food is too big
 63 |                  Xfood = exp(-1/P).*Xfood;   %Eq.(12)
 64 |                 for j = 1:dim
 65 |                     Xnew(i,j) = X(i,j)+cos(2*pi*rand)*Xfood(j)*p_obj(temp)-sin(2*pi*rand)*Xfood(j)*p_obj(temp); %Eq.(13)
 66 |                 end
 67 |             else
 68 |                 Xnew(i,:) = (X(i,:)-Xfood)*p_obj(temp)+p_obj(temp).*rand(1,dim).*X(i,:); %Eq.(14)
 69 |             end
 70 |         end
 71 |     end
 72 |     %% boundary conditions
 73 |     for i=1:N
 74 |         for j =1:dim
 75 |             if length(ub)==1
 76 |                 Xnew(i,j) = min(ub,Xnew(i,j));
 77 |                 Xnew(i,j) = max(lb,Xnew(i,j));
 78 |             else
 79 |                 Xnew(i,j) = min(ub(j),Xnew(i,j));
 80 |                 Xnew(i,j) = max(lb(j),Xnew(i,j));
 81 |             end
 82 |         end
 83 |     end
 84 |    
 85 |     global_position = Xnew(1,:);
 86 |     global_fitness = fobj(global_position);
 87 |  
 88 |     for i =1:N
 89 |          %% Obtain the optimal solution for the updated population
 90 |         new_fitness = fobj(Xnew(i,:));
 91 |         if new_fitness<global_fitness
 92 |                  global_fitness = new_fitness;
 93 |                  global_position = Xnew(i,:);
 94 |         end
 95 |         %% Update the population to a new location
 96 |         if new_fitness<fitness_f(i)
 97 |              fitness_f(i) = new_fitness;
 98 |              X(i,:) = Xnew(i,:);
 99 |              if fitness_f(i)<Best_fitness
100 |                  Best_fitness=fitness_f(i);
101 |                  best_position = X(i,:);
102 |              end
103 |         end
104 |     end
105 |     global_Cov(t) = global_fitness;
106 |     cuve_f(t) = Best_fitness;
107 |     t=t+1;
108 |     if mod(t,50)==0
109 |       disp("COA"+"iter"+num2str(t)+": "+Best_fitness); 
110 |    end
111 | end
112 |  best_fun = Best_fitness;
113 | end
114 | function y = p_obj(x)   %Eq.(4)
115 |     y = 0.2*(1/(sqrt(2*pi)*3))*exp(-(x-25).^2/(2*3.^2));
116 | end


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/COA/Get_F.m:
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  1 | 
  2 | 
  3 | function [LB,UB,Dim,F_obj] = Get_F(F)
  4 | 
  5 | 
  6 | switch F
  7 |     case 'F1'
  8 |         F_obj = @F1;
  9 |         LB=-100;
 10 |         UB=100;
 11 |         Dim=10;
 12 |         
 13 |     case 'F2'
 14 |         F_obj = @F2;
 15 |         LB=-10;
 16 |         UB=10;
 17 |         Dim=10;
 18 |         
 19 |     case 'F3'
 20 |         F_obj = @F3;
 21 |         LB=-100;
 22 |         UB=100;
 23 |         Dim=10;
 24 |         
 25 |     case 'F4'
 26 |         F_obj = @F4;
 27 |         LB=-100;
 28 |         UB=100;
 29 |         Dim=10;
 30 |         
 31 |     case 'F5'
 32 |         F_obj = @F5;
 33 |         LB=-30;
 34 |         UB=30;
 35 |         Dim=10;
 36 |         
 37 |     case 'F6'
 38 |         F_obj = @F6;
 39 |         LB=-100;
 40 |         UB=100;
 41 |         Dim=10;
 42 |         
 43 |     case 'F7'
 44 |         F_obj = @F7;
 45 |         LB=-1.28;
 46 |         UB=1.28;
 47 |         Dim=10;
 48 |         
 49 |     case 'F8'
 50 |         F_obj = @F8;
 51 |         LB=-500;
 52 |         UB=500;
 53 |         Dim=10;
 54 |         
 55 |     case 'F9'
 56 |         F_obj = @F9;
 57 |         LB=-5.12;
 58 |         UB=5.12;
 59 |         Dim=10;
 60 |         
 61 |     case 'F10'
 62 |         F_obj = @F10;
 63 |         LB=-32;
 64 |         UB=32;
 65 |         Dim=10;
 66 |         
 67 |     case 'F11'
 68 |         F_obj = @F11;
 69 |         LB=-600;
 70 |         UB=600;
 71 |         Dim=10;
 72 |         
 73 |     case 'F12'
 74 |         F_obj = @F12;
 75 |         LB=-50;
 76 |         UB=50;
 77 |         Dim=10;
 78 |         
 79 |     case 'F13'
 80 |         F_obj = @F13;
 81 |         LB=-50;
 82 |         UB=50;
 83 |         Dim=10;
 84 |         
 85 |     case 'F14'
 86 |         F_obj = @F14;
 87 |         LB=-65.536;
 88 |         UB=65.536;
 89 |         Dim=2;
 90 |         
 91 |     case 'F15'
 92 |         F_obj = @F15;
 93 |         LB=-5;
 94 |         UB=5;
 95 |         Dim=4;
 96 |         
 97 |     case 'F16'
 98 |         F_obj = @F16;
 99 |         LB=-5;
100 |         UB=5;
101 |         Dim=2;
102 |         
103 |     case 'F17'
104 |         F_obj = @F17;
105 |         LB=[-5,0];
106 |         UB=[10,15];
107 |         Dim=2;
108 |         
109 |     case 'F18'
110 |         F_obj = @F18;
111 |         LB=-2;
112 |         UB=2;
113 |         Dim=2;
114 |         
115 |     case 'F19'
116 |         F_obj = @F19;
117 |         LB=0;
118 |         UB=1;
119 |         Dim=3;
120 |         
121 |     case 'F20'
122 |         F_obj = @F20;
123 |         LB=0;
124 |         UB=1;
125 |         Dim=6;     
126 |         
127 |     case 'F21'
128 |         F_obj = @F21;
129 |         LB=0;
130 |         UB=10;
131 |         Dim=4;    
132 |         
133 |     case 'F22'
134 |         F_obj = @F22;
135 |         LB=0;
136 |         UB=10;
137 |         Dim=4;    
138 |         
139 |     case 'F23'
140 |         F_obj = @F23;
141 |         LB=0;
142 |         UB=10;
143 |         Dim=4;            
144 | end
145 | 
146 | end
147 | 
148 | % F1
149 | 
150 | function o = F1(x)
151 | o=sum(x.^2);
152 | end
153 | 
154 | % F2
155 | 
156 | function o = F2(x)
157 | o=sum(abs(x))+prod(abs(x));
158 | end
159 | 
160 | % F3
161 | 
162 | function o = F3(x)
163 | dim=size(x,2);
164 | o=0;
165 | for i=1:dim
166 |     o=o+sum(x(1:i))^2;
167 | end
168 | end
169 | 
170 | % F4
171 | 
172 | function o = F4(x)
173 | o=max(abs(x));
174 | end
175 | 
176 | % F5
177 | 
178 | function o = F5(x)
179 | dim=size(x,2);
180 | o=sum(100*(x(2:dim)-(x(1:dim-1).^2)).^2+(x(1:dim-1)-1).^2);
181 | end
182 | 
183 | % F6
184 | 
185 | function o = F6(x)
186 | o=sum(abs((x+.5)).^2);
187 | end
188 | 
189 | % F7
190 | 
191 | function o = F7(x)
192 | dim=size(x,2);
193 | o=sum(1:dim.*(x.^4))+rand;
194 | end
195 | 
196 | % F8
197 | 
198 | function o = F8(x)
199 | o=sum(-x.*sin(sqrt(abs(x))));
200 | end
201 | 
202 | % F9
203 | 
204 | function o = F9(x)
205 | dim=size(x,2);
206 | o=sum(x.^2-10*cos(2*pi.*x))+10*dim;
207 | end
208 | 
209 | % F10
210 | 
211 | function o = F10(x)
212 | dim=size(x,2);
213 | o=-20*exp(-.2*sqrt(sum(x.^2)/dim))-exp(sum(cos(2*pi.*x))/dim)+20+exp(1);
214 | end
215 | 
216 | % F11
217 | 
218 | function o = F11(x)
219 | dim=size(x,2);
220 | o=sum(x.^2)/4000-prod(cos(x./sqrt([1:dim])))+1;
221 | end
222 | 
223 | % F12
224 | 
225 | function o = F12(x)
226 | dim=size(x,2);
227 | o=(pi/dim)*(10*((sin(pi*(1+(x(1)+1)/4)))^2)+sum((((x(1:dim-1)+1)./4).^2).*...
228 | (1+10.*((sin(pi.*(1+(x(2:dim)+1)./4)))).^2))+((x(dim)+1)/4)^2)+sum(Ufun(x,10,100,4));
229 | end
230 | 
231 | % F13
232 | 
233 | function o = F13(x)
234 | dim=size(x,2);
235 | o=.1*((sin(3*pi*x(1)))^2+sum((x(1:dim-1)-1).^2.*(1+(sin(3.*pi.*x(2:dim))).^2))+...
236 | ((x(dim)-1)^2)*(1+(sin(2*pi*x(dim)))^2))+sum(Ufun(x,5,100,4));
237 | end
238 | 
239 | % F14
240 | 
241 | function o = F14(x)
242 | aS=[-32 -16 0 16 32 -32 -16 0 16 32 -32 -16 0 16 32 -32 -16 0 16 32 -32 -16 0 16 32;,...
243 | -32 -32 -32 -32 -32 -16 -16 -16 -16 -16 0 0 0 0 0 16 16 16 16 16 32 32 32 32 32];
244 | 
245 | for j=1:25
246 |     bS(j)=sum((x'-aS(:,j)).^6);
247 | end
248 | o=(1/500+sum(1./([1:25]+bS))).^(-1);
249 | end
250 | 
251 | % F15
252 | 
253 | function o = F15(x)
254 | aK=[.1957 .1947 .1735 .16 .0844 .0627 .0456 .0342 .0323 .0235 .0246];
255 | bK=[.25 .5 1 2 4 6 8 10 12 14 16];bK=1./bK;
256 | o=sum((aK-((x(1).*(bK.^2+x(2).*bK))./(bK.^2+x(3).*bK+x(4)))).^2);
257 | end
258 | 
259 | % F16
260 | 
261 | function o = F16(x)
262 | o=4*(x(1)^2)-2.1*(x(1)^4)+(x(1)^6)/3+x(1)*x(2)-4*(x(2)^2)+4*(x(2)^4);
263 | end
264 | 
265 | % F17
266 | 
267 | function o = F17(x)
268 | o=(x(2)-(x(1)^2)*5.1/(4*(pi^2))+5/pi*x(1)-6)^2+10*(1-1/(8*pi))*cos(x(1))+10;
269 | end
270 | 
271 | % F18
272 | 
273 | function o = F18(x)
274 | o=(1+(x(1)+x(2)+1)^2*(19-14*x(1)+3*(x(1)^2)-14*x(2)+6*x(1)*x(2)+3*x(2)^2))*...
275 |     (30+(2*x(1)-3*x(2))^2*(18-32*x(1)+12*(x(1)^2)+48*x(2)-36*x(1)*x(2)+27*(x(2)^2)));
276 | end
277 | 
278 | % F19
279 | 
280 | function o = F19(x)
281 | aH=[3 10 30;.1 10 35;3 10 30;.1 10 35];cH=[1 1.2 3 3.2];
282 | pH=[.3689 .117 .2673;.4699 .4387 .747;.1091 .8732 .5547;.03815 .5743 .8828];
283 | o=0;
284 | for i=1:4
285 |     o=o-cH(i)*exp(-(sum(aH(i,:).*((x-pH(i,:)).^2))));
286 | end
287 | end
288 | 
289 | % F20
290 | 
291 | function o = F20(x)
292 | aH=[10 3 17 3.5 1.7 8;.05 10 17 .1 8 14;3 3.5 1.7 10 17 8;17 8 .05 10 .1 14];
293 | cH=[1 1.2 3 3.2];
294 | pH=[.1312 .1696 .5569 .0124 .8283 .5886;.2329 .4135 .8307 .3736 .1004 .9991;...
295 | .2348 .1415 .3522 .2883 .3047 .6650;.4047 .8828 .8732 .5743 .1091 .0381];
296 | o=0;
297 | for i=1:4
298 |     o=o-cH(i)*exp(-(sum(aH(i,:).*((x-pH(i,:)).^2))));
299 | end
300 | end
301 | 
302 | % F21
303 | 
304 | function o = F21(x)
305 | aSH=[4 4 4 4;1 1 1 1;8 8 8 8;6 6 6 6;3 7 3 7;2 9 2 9;5 5 3 3;8 1 8 1;6 2 6 2;7 3.6 7 3.6];
306 | cSH=[.1 .2 .2 .4 .4 .6 .3 .7 .5 .5];
307 | 
308 | o=0;
309 | for i=1:5
310 |     o=o-((x-aSH(i,:))*(x-aSH(i,:))'+cSH(i))^(-1);
311 | end
312 | end
313 | 
314 | % F22
315 | 
316 | function o = F22(x)
317 | aSH=[4 4 4 4;1 1 1 1;8 8 8 8;6 6 6 6;3 7 3 7;2 9 2 9;5 5 3 3;8 1 8 1;6 2 6 2;7 3.6 7 3.6];
318 | cSH=[.1 .2 .2 .4 .4 .6 .3 .7 .5 .5];
319 | 
320 | o=0;
321 | for i=1:7
322 |     o=o-((x-aSH(i,:))*(x-aSH(i,:))'+cSH(i))^(-1);
323 | end
324 | end
325 | 
326 | % F23
327 | 
328 | function o = F23(x)
329 | aSH=[4 4 4 4;1 1 1 1;8 8 8 8;6 6 6 6;3 7 3 7;2 9 2 9;5 5 3 3;8 1 8 1;6 2 6 2;7 3.6 7 3.6];
330 | cSH=[.1 .2 .2 .4 .4 .6 .3 .7 .5 .5];
331 | 
332 | o=0;
333 | for i=1:10
334 |     o=o-((x-aSH(i,:))*(x-aSH(i,:))'+cSH(i))^(-1);
335 | end
336 | end
337 | 
338 | function o=Ufun(x,a,k,m)
339 | o=k.*((x-a).^m).*(x>a)+k.*((-x-a).^m).*(x<(-a));
340 | end


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