一、获取代码方式
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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】蛾群优化算法（MSA）【含Matlab源码 1451期】
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二、部分源代码
%______________________________________________________________________________________________
% Moth Swarm Algorithm (MSA)
% Developed in MATLAB R2008b
%
%
%
%_______________________________________________________________________________________________
% This algorithm has a fast convergence characteristics.
% Appreciated results can be obtained with a small number of iterations.
for iji=[1,8,21]
if iji==1;F=('F1');elseif iji==2;F=('F2');elseif iji==3;F=('F3');elseif iji==4;F=('F4');elseif iji==5;F=('F5'); ...
elseif iji==6;F=('F6');elseif iji==7; F=('F7'); elseif iji==8; F=('F8');elseif iji==9; F=('F9'); ...
elseif iji==10; F=('F10');elseif iji==11; F=('F11');elseif iji==12; F=('F12'); ...
elseif iji==13; F=('F13');elseif iji==14; F=('F14');elseif iji==15; F=('F15');
elseif iji==16; F=('F16');elseif iji==17; F=('F17');elseif iji==18; F=('F18');
elseif iji==19; F=('F19');elseif iji==20; F=('F20');elseif iji==21; F=('F21');
elseif iji==22; F=('F22');elseif iji==23; F=('F23');
end
if iji < 14;Max_iteration=1000;else Max_iteration=500;end% Maximum number of iterations
SearchAgents_no=30;% Number of search agents
Nc=6;% Number of Pathfinders: 4 <= Nc <= 20% of SearchAgents_no
% Load details of the selected benchmark function
[lb,ub,dim,fobj]=Get_Functions_details(F);
[Best_pos,Best_score,Convergence_curve]=MSA(SearchAgents_no,Nc,Max_iteration,ub,lb,dim,fobj);
%Draw and display objective function
figure,semilogy(Convergence_curve); title( F ); xlabel('Iteration'); ylabel('Best score');
display(['The optimal solution of ',F, ' is: ',num2str(Best_pos)]);
display(['The optimal value of ',F,' is : ', num2str(Best_score)]);
end
% =====================================================
%______________________________________________________________________________________________
% Moth Swarm Algorithm (MSA)
% Developed in MATLAB R2008b
%
% Author and programmer: Al-Attar Ali Mohamed
%
% e-Mail: engatar@yahoo.com
%_______________________________________________________________________________________________
% reference:
% [1] X. Yao, Y. Liu, G. Lin, Evolutionary programming made faster, IEEE Trans.Evolution. Comput. 3 (2) (1999) 82?02.
% [2] Salimi H. Stochastic Fractal Search: A powerful metaheuristic algorithm. Knowledge Based Syst 2015; 75: 1-18.?doi:10.1016/j.knosys.2014.07.025
% lb is the lower bound: lb=[lb_1,lb_2,...,lb_d]
% up is the uppper bound: ub=[ub_1,ub_2,...,ub_d]
% dim is the number of variables (dimension of the problem)
function [lb,ub,dim,fobj] = Get_Functions_details(F)
switch F
case 'F1'
fobj = @F1;
lb=-100;
ub=100;
dim=30;
case 'F2'
fobj = @F2;
lb=-10;
ub=10;
dim=30;
case 'F3'
fobj = @F3;
lb=-100;
ub=100;
dim=30;
case 'F4'
fobj = @F4;
lb=-100;
ub=100;
dim=30;
case 'F5'
fobj = @F5;
lb=-30;
ub=30;
dim=30;
case 'F6'
fobj = @F6;
lb=-100;
ub=100;
dim=30;
case 'F7'
fobj = @F7;
lb=-1.28;
ub=1.28;
dim=30;
case 'F8'
fobj = @F8;
lb=-500;
ub=500;
dim=30;
case 'F9'
fobj = @F9;
lb=-5.12;
ub=5.12;
dim=30;
case 'F10'
fobj = @F10;
lb=-32;
ub=32;
dim=30;
case 'F11'
fobj = @F11;
lb=-600;
ub=600;
dim=30;
case 'F12'
fobj = @F12;
lb=-50;
ub=50;
dim=30;
case 'F13'
fobj = @F13;
lb=-50;
ub=50;
dim=30;
case 'F14'
fobj = @F14;
lb=-65.536;
ub=65.536;
dim=2;
case 'F15'
fobj = @F15;
lb=-5;
ub=5;
dim=4;
case 'F16'
fobj = @F16;
lb=-5;
ub=5;
dim=2;
case 'F17'
fobj = @F17;
lb=[-5,0];
ub=[10,15];
dim=2;
case 'F18'
fobj = @F18;
lb=-5;
ub=5;
dim=2;
case 'F19'
fobj = @F19;
lb=0;
ub=1;
dim=3;
case 'F20'
fobj = @F20;
lb=0;
ub=1;
dim=6;
case 'F21'
fobj = @F21;
lb=0;
ub=10;
dim=4;
case 'F22'
fobj = @F22;
lb=0;
ub=10;
dim=4;
case 'F23'
fobj = @F23;
lb=0;
ub=10;
dim=4;
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.
