一、获取代码方式
获取代码方式1： 通过订阅紫极神光博客付费专栏，凭支付凭证，私信博主，可获得此代码。
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获取代码方式3： 完整代码已上传我的资源：【优化算法】调谐群体优化算法（TSA）【含Matlab源码 1429期】
备注：开通CSDN会员，仅只能免费获得1份代码（有效期为开通日起，三天内有效）； 订阅紫极神光博客付费专栏，可免费获得2份代码（有效期为订阅日起，三天内有效）；
二、部分源代码
%%%
clear all
clc
SearchAgents=30;
Fun_name='F1';
Max_iterations=1000;
[lowerbound,upperbound,dimension,fitness]=fun_info(Fun_name);
[Best_score,Best_pos,TSA_curve]=tsa(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness);
figure('Position',[400 400 560 190])
subplot(1,2,1);
func_plot(Fun_name);
title('Objective space')
xlabel('x_1');
ylabel('x_2');
zlabel([Fun_name,'( x_1 , x_2 )'])
subplot(1,2,2);
plots=semilogx(TSA_curve,'Color','g');
set(plots,'linewidth',2)
hold on
title('Objective space')
xlabel('Iterations');
ylabel('Best score');
axis tight
grid on
box on
legend('TSA')
display(['The best solution obtained by TSA is : ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by TSA is : ', num2str(Best_score)]);
%%% Designed and Developed by Dr. Gaurav Dhiman (http://dhimangaurav.com/) %%%
function [lowerbound,upperbound,dimension,fitness] = fun_info(F)
switch F
case 'F1'
fitness = @F1;
lowerbound=-100;
upperbound=100;
dimension=30;
case 'F2'
fitness = @F2;
lowerbound=-10;
upperbound=10;
dimension=30;
case 'F3'
fitness = @F3;
lowerbound=-100;
upperbound=100;
dimension=30;
case 'F4'
fitness = @F4;
lowerbound=-100;
upperbound=100;
dimension=30;
case 'F5'
fitness = @F5;
lowerbound=-30;
upperbound=30;
dimension=30;
case 'F6'
fitness = @F6;
lowerbound=-100;
upperbound=100;
dimension=30;
case 'F7'
fitness = @F7;
lowerbound=-1.28;
upperbound=1.28;
dimension=30;
case 'F8'
fitness = @F8;
lowerbound=-500;
upperbound=500;
dimension=30;
case 'F9'
fitness = @F9;
lowerbound=-5.12;
upperbound=5.12;
dimension=30;
case 'F10'
fitness = @F10;
lowerbound=-32;
upperbound=32;
dimension=30;
case 'F11'
fitness = @F11;
lowerbound=-600;
upperbound=600;
dimension=30;
case 'F12'
fitness = @F12;
lowerbound=-50;
upperbound=50;
dimension=30;
case 'F13'
fitness = @F13;
lowerbound=-50;
upperbound=50;
dimension=30;
case 'F14'
fitness = @F14;
lowerbound=-65.536;
upperbound=65.536;
dimension=2;
case 'F15'
fitness = @F15;
lowerbound=-5;
upperbound=5;
dimension=4;
case 'F16'
fitness = @F16;
lowerbound=-5;
upperbound=5;
dimension=2;
case 'F17'
fitness = @F17;
lowerbound=[-5,0];
upperbound=[10,15];
dimension=2;
case 'F18'
fitness = @F18;
lowerbound=-2;
upperbound=2;
dimension=2;
case 'F19'
fitness = @F19;
lowerbound=0;
upperbound=1;
dimension=3;
case 'F20'
fitness = @F20;
lowerbound=0;
upperbound=1;
dimension=6;
case 'F21'
fitness = @F21;
lowerbound=0;
upperbound=10;
dimension=4;
case 'F22'
fitness = @F22;
lowerbound=0;
upperbound=10;
dimension=4;
case 'F23'
fitness = @F23;
lowerbound=0;
upperbound=10;
dimension=4;
end
end
% F1
function R = F1(x)
R=sum(x.^2);
end
% F2
function R = F2(x)
R=sum(abs(x))+prod(abs(x));
end
% F3
function R = F3(x)
dimension=size(x,2);
R=0;
for i=1:dimension
R=R+sum(x(1:i))^2;
end
end
% F4
function R = F4(x)
R=max(abs(x));
end
% F5
function R = F5(x)
dimension=size(x,2);
R=sum(100*(x(2:dimension)-(x(1:dimension-1).^2)).^2+(x(1:dimension-1)-1).^2);
end
% F6
function R = F6(x)
R=sum(abs((x+.5)).^2);
end
% F7
function R = F7(x)
dimension=size(x,2);
R=sum([1:dimension].*(x.^4))+rand;
end
% F8
function R = F8(x)
R=sum(-x.*sin(sqrt(abs(x))));
end
% F9
function R = F9(x)
dimension=size(x,2);
R=sum(x.^2-10*cos(2*pi.*x))+10*dimension;
end
% F10
function R = F10(x)
dimension=size(x,2);
R=-20*exp(-.2*sqrt(sum(x.^2)/dimension))-exp(sum(cos(2*pi.*x))/dimension)+20+exp(1);
end
% F11
function R = F11(x)
dimension=size(x,2);
R=sum(x.^2)/4000-prod(cos(x./sqrt([1:dimension])))+1;
end
% F12
function R = F12(x)
dimension=size(x,2);
R=(pi/dimension)*(10*((sin(pi*(1+(x(1)+1)/4)))^2)+sum((((x(1:dimension-1)+1)./4).^2).*...
(1+10.*((sin(pi.*(1+(x(2:dimension)+1)./4)))).^2))+((x(dimension)+1)/4)^2)+sum(Ufun(x,10,100,4));
end
% F13
function R = F13(x)
dimension=size(x,2);
R=.1*((sin(3*pi*x(1)))^2+sum((x(1:dimension-1)-1).^2.*(1+(sin(3.*pi.*x(2:dimension))).^2))+...
((x(dimension)-1)^2)*(1+(sin(2*pi*x(dimension)))^2))+sum(Ufun(x,5,100,4));
end
% F14
function R = F14(x)
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;,...
-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];
for j=1:25
bS(j)=sum((x'-aS(:,j)).^6);
end
R=(1/500+sum(1./([1:25]+bS))).^(-1);
end
% F15
function R = F15(x)
aK=[.1957 .1947 .1735 .16 .0844 .0627 .0456 .0342 .0323 .0235 .0246];
bK=[.25 .5 1 2 4 6 8 10 12 14 16];bK=1./bK;
R=sum((aK-((x(1).*(bK.^2+x(2).*bK))./(bK.^2+x(3).*bK+x(4)))).^2);
end
% F16
function R = F16(x)
R=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);
end
% F17
function R = F17(x)
R=(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;
end
% F18
function R = F18(x)
R=(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))*...
(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)));
end
% F19
function R = F19(x)
aH=[3 10 30;.1 10 35;3 10 30;.1 10 35];cH=[1 1.2 3 3.2];
pH=[.3689 .117 .2673;.4699 .4387 .747;.1091 .8732 .5547;.03815 .5743 .8828];
R=0;
for i=1:4
R=R-cH(i)*exp(-(sum(aH(i,:).*((x-pH(i,:)).^2))));
end
end
% F20
function R = F20(x)
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];
cH=[1 1.2 3 3.2];
pH=[.1312 .1696 .5569 .0124 .8283 .5886;.2329 .4135 .8307 .3736 .1004 .9991;...
.2348 .1415 .3522 .2883 .3047 .6650;.4047 .8828 .8732 .5743 .1091 .0381];
R=0;
for i=1:4
R=R-cH(i)*exp(-(sum(aH(i,:).*((x-pH(i,:)).^2))));
end
end
三、运行结果
四、matlab版本及参考文献
1 matlab版本 2014a
2 参考文献 [1] 包子阳,余继周,杨杉.智能优化算法及其MATLAB实例（第2版）[M].电子工业出版社，2016. [2]张岩,吴水根.MATLAB优化算法源代码[M].清华大学出版社，2017.
