【优化算法】自治群体粒子群优化算法（AGPSO）【含Matlab源码 1450期】

一、获取代码方式

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二、部分源代码

%  Autonomous Groups Particles Swarm Optimization (AGPSO) source codes version 1.1   %
%                                                                                    %
%  Developed in MATLAB R2014a(7.13)                                                  %
%                                                                                    %
                   %

% You can simply define your cost in a seperate file and load its handle to fobj 
% The initial parameters that you need are:
%__________________________________________
% fobj = @YourCostFunction
% dim = number of your variables
% Max_iteration = maximum number of generations
% SearchAgents_no = number of search agents
% lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n
% ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n
% If all the variables have equal lower bound you can just
% define lb and ub as two single number numbers

% To run AGPSO3: [Best_score,Best_pos,GWO_cg_curve]=AGPSO3(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)
%__________________________________________

clear all 
clc

SearchAgents_no=30; % Number of search agents

Function_name='F8'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper)

Max_iteration=500; % Maximum numbef of iterations

% Load details of the selected benchmark function
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);

[Best_score1,Best_pos1,AGPSO1_cg_curve]= AGPSO1(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);

[Best_score2,Best_pos2,AGPSO2_cg_curve]= AGPSO2(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);

[Best_score3,Best_pos3,AGPSO3_cg_curve]= AGPSO3(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);

[Best_score4,Best_pos4,PSO_cg_curve]   = PSO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);

[Best_score5,Best_pos5,IPSO_cg_curve]= IPSO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);

[Best_score6,Best_pos6,TACPSO_cg_curve]= TACPSO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);

[Best_score7,Best_pos7,MPSO_cg_curve]= MPSO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);

figure('Position',[300 300 660 290])

%Draw search space
subplot(1,2,1);
func_plot(Function_name);
title('Parameter space')
xlabel('x_1');
ylabel('x_2');
zlabel([Function_name,'( x_1 , x_2 )'])

%Draw convergence curves
subplot(1,2,2);
semilogy(AGPSO1_cg_curve,'Color','r')
hold on
semilogy(AGPSO2_cg_curve,'Color','b')
semilogy(AGPSO3_cg_curve,'Color','k')
semilogy(PSO_cg_curve,'Color','g')
semilogy(MPSO_cg_curve,'Color','y')
semilogy(TACPSO_cg_curve,'Color','c')
semilogy(IPSO_cg_curve,'Color','m')

title('Objective space')
xlabel('Iteration');
ylabel('Best score obtained so far');

axis tight
grid on
box on
legend('AGPSO1','AGPSO2','AGPSO3', 'PSO', 'MPSO', 'TACPSO', 'IPSO')

display(['The best solution obtained by AGPSO1 is : ', num2str(Best_pos1)]);
display(['The best optimal value obtained by AGPSO1 is : ', num2str(Best_score1)]);

display(['The best solution obtained by AGPSO2 is : ', num2str(Best_pos2)]);
display(['The best optimal value obtained by AGPSO2 is : ', num2str(Best_score2)]);

display(['The best solution obtained by AGPSO3 is : ', num2str(Best_pos3)]);
display(['The best optimal value obtained by AGPSO3 is : ', num2str(Best_score3)]);

display(['The best solution obtained by SPSO is : ', num2str(Best_pos4)]);
display(['The best optimal value obtained by SPSO is : ', num2str(Best_score4)]);

display(['The best solution obtained by MPSO is : ', num2str(Best_pos5)]);
display(['The best optimal value obtained by MPSO is : ', num2str(Best_score5)]);

display(['The best solution obtained by TACPSO is : ', num2str(Best_pos6)]);
display(['The best optimal value obtained by TACPSO is : ', num2str(Best_score6)]);

display(['The best solution obtained by IPSO is : ', num2str(Best_pos1)]);
display(['The best optimal value obtained by IPSO is : ', num2str(Best_score1)]);
function [gBestScore,gBest,cg_curve]=IPSO(N,Max_iteration,lb,ub,dim,fobj)

wMax=0.9;
wMin=0.4;
c1=2;
c2=2;

vel=zeros(N,dim);
pos=zeros(N,dim);
pBestScore=zeros(N);
pBest=zeros(N,dim);
gBestScore=0;
gBest=zeros(1,dim);

%Initialization
for i=1:size(pos,1) 
    for j=1:size(pos,2) 
        pos(i,j)=(ub(j)-lb(j))*rand()+lb(j);
        vel(i,j)=0.3*rand();
    end
end
for i=1:N
    pBestScore(i)=inf;
end

%initialize gBestScore for min
gBestScore=inf;
     
    
for l=1:Max_iteration
    %Calculate Score Function
    for i=1:size(pos,1)  
        fitness=0;
        
        Tp=pos(i,:)>ub;Tm=pos(i,:)<lb;pos(i,:)=(pos(i,:).*(~(Tp+Tm)))+ub.*Tp+lb.*Tm;                   
        
        fitness=fobj(pos(i,:));
       
        if(pBestScore(i)>fitness)
            pBestScore(i)=fitness;
            pBest(i,:)=pos(i,:);
        end
        if(gBestScore>fitness)
            gBestScore=fitness;
            gBest=pos(i,:);
        end
    end

    c1=2.5+2*(l/Max_iteration)^2-2*(2*l/Max_iteration);
    c2=3-c1;  
    
    %update the W of PSO
    w=wMax-l*((wMax-wMin)/Max_iteration);
    
    %Update the Velocity and Position of particles
    for i=1:size(pos,1)
        for j=1:size(pos,2)       
            vel(i,j)=w*vel(i,j)+c1*rand()*(pBest(i,j)-pos(i,j))+c2*rand()*(gBest(j)-pos(i,j));
            pos(i,j)=pos(i,j)+vel(i,j);
        end
    end
    cg_curve(l)=gBestScore;
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.

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