【优化算法】蛾群优化算法（MSA）【含Matlab源码 1451期】-CFANZ编程社区

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%______________________________________________________________________________________________
%  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