1 简介
本文通过结合引力搜索算法和粒子群优化算法，克服单一算法的不足，发挥单个算法的优点，提出一种改进的混合优化算法( Integrated Gravitation- al Search Algorithm and Particle Swarm Optimization， IN-GSA-PSO) 。
2 部分代码
clear all
close all
clc
Q=1; % ACO Parameter
tau0=10; % Initial Phromone (ACO)
alpha=0.3; % Phromone Exponential Weight (ACO)
rho=0.1; % Evaporation Rate (ACO)
beta_min=0.2; % Lower Bound of Scaling Factor (DE)
beta_max=0.8; % Upper Bound of Scaling Factor (DE)
pCR=0.2; % Crossover Probability (DE)
Runno=10;
SearchAgents_no=20; % Number of search agents
% classification datasets
Function_name='F1'; %MLP_XOR dataset
% Function_name='F2'; %MLP_Baloon dataset
% Function_name='F3'; %MLP_Iris dataset
% Function_name='F4'; %MLP_Cancer dataset
% Function_name='F5'; %MLP_Heart dataset
% Function approximation datasets
% Function_name='F6'; %MLP_Sigmoid dataset
% Function_name='F7'; %MLP_Cosine dataset
% Function_name='F8'; %MLP_Sine dataset
% Function_name='F9'; %MLP_Sphere dataset
% Load details of the selected data set
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);
ElitistCheck=1;
min_flag=1;
Rpower=1;
Max_iteration=500; % Maximum numbef of iterations
%
if Function_name=='F1'
input= [0 0 0 0 1 1 1 1;0 0 1 1 0 0 1 1;0 1 0 1 0 1 0 1];
target3=[0 1 1 0 1 0 0 1];
Hno=7;
dim = 5*7+1; % Dimension of the problem
for i=1:1:Runno
Rrate=0;
[Fbest,Lbest,BestChart]=GSA(SearchAgents_no,Max_iteration,ElitistCheck,min_flag,Rpower,lb,ub,dim,fobj);
BestSolutions1(i) = Fbest;
[gBestScore,gBest,GlobalBestCost]= CPSOGSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
BestSolutions4(i) = gBestScore;
[BestSolACO,BestAnt,BestCostACO] = ACO(SearchAgents_no, Max_iteration,Q,tau0,alpha,rho,lb,ub,dim,fobj);
BestSolutions5(i) = BestSolACO.Cost;
[BestCost,Best_Hab,BestSol] = bbo( SearchAgents_no, Max_iteration,lb,ub,dim,fobj);
BestSolutions6(i) = BestSol.Cost;
[BestSolDE,DBestSol,BestCostDE] = DE(SearchAgents_no, Max_iteration,beta_min,beta_max,pCR,lb,ub,dim,fobj);
BestSolutions7(i) = BestSolDE.Cost ;
W=Lbest(1:4*Hno);
B=Lbest(4*Hno+1:dim);
W=gBest(1:4*Hno);
B=gBest(4*Hno+1:dim);
W=BestAnt(1:4*Hno);
B=BestAnt(4*Hno+1:dim);
W=Best_Hab(1:4*Hno);
B=Best_Hab(4*Hno+1:dim);
W=DBestSol(1:4*Hno);
B=DBestSol(4*Hno+1:dim);
for pp=1:8
actualvalue=my_simulate(3,Hno,1,W,B,input(:,pp)');
if(target3(pp)==1)
if (actualvalue>=0.95)
Rrate=Rrate+1;
end
end
if(target3(pp)==0)
if (actualvalue(1)<0.05)
Rrate=Rrate+1;
end
end
end
Classification_rate(i)=(Rrate/8)*100;
disp(['GSA is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(Fbest),' Mean_Classification rate = ' , num2str(Classification_rate(i)),')'])
% disp(['CPSOGSA is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(gBestScore),' Mean_Classification rate = ' , num2str(Classification_rate(i)),')'])
% disp(['ACO is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(BestSolACO.Cost),' Mean_Classification rate =' , num2str(Classification_rate(i)),')'])
% disp(['BBO is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(BestSol.Cost),' Mean_Classification rate = ' , num2str(Classification_rate(i)),')'])
% disp(['DE is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(BestSolDE.Cost),' Mean_Classification rate = ' , num2str(Classification_rate(i)),')'])
end
A_Classification_rate=mean(Classification_rate);
Average= mean(BestSolutions1);
StandDP=std(BestSolutions1);
Med = median(BestSolutions1);
[BestValueP I] = min(BestSolutions1);
[WorstValueP IM]=max(BestSolutions1);
end
%
% if Function_name=='F2'
%
% load baloon.txt
% x=sortrows(baloon,2);
% %I2=x(1:150,1:4);
% I2(:,1)=x(1:20,1);
% I2(:,2)=x(1:20,2);
% I2(:,3)=x(1:20,3);
% I2(:,4)=x(1:20,4);
% T=x(1:20,5);
%
%
% Hno=9;
% dim = 6*9+1;
%
% for i=1:1:Runno
% [Fbest,Lbest,BestChart]=GSA(SearchAgents_no,Max_iteration,ElitistCheck,min_flag,Rpower,lb,ub,dim,fobj);
% BestSolutions1(i) = Fbest;
% [gBestScore,gBest,GlobalBestCost]= CPSOGSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
% BestSolutions4(i) = gBestScore;
% [BestSolACO,BestAnt,BestCostACO] = ACO(SearchAgents_no, Max_iteration,Q,tau0,alpha,rho,lb,ub,dim,fobj);
% BestSolutions5(i) = BestSolACO.Cost;
% [BestCost,Best_Hab,BestSol] = bbo( SearchAgents_no, Max_iteration,lb,ub,dim,fobj);
% BestSolutions6(i) = BestSol.Cost;
% [BestSolDE,DBestSol,BestCostDE] = DE(SearchAgents_no, Max_iteration,beta_min,beta_max,pCR,lb,ub,dim,fobj);
% BestSolutions7(i) = BestSolDE.Cost ;
% Rrate=0;
%W=Lbest(1:45);
%B=Lbest(46:55);
%W=gBest(1:45);
%B=gBest(46:55);
%W=BestAnt(1:45);
% B=BestAnt(46:55);
% W=Best_Hab(1:45);
% B=Best_Hab(46:55);
% W=DBestSol(1:45);
% B=DBestSol(46:55);
% for pp=1:20
% actualvalue=my_simulate(4,9,1,W,B,I2(pp,:));
% if(T(pp)==1)
% if (actualvalue>=0.95)
% Rrate=Rrate+1;
% end
% end
% if(T(pp)==0)
% if (actualvalue(1)<0.05)
% Rrate=Rrate+1;
% end
% end
%
% end
% end
% end
% % %
% if Function_name=='F3'
%
% load iris.txt;
% x=sortrows(iris,2);
% I2=x(1:150,1:4);
% H2=x(1:150,1);
% H3=x(1:150,2);
% H4=x(1:150,3);
% H5=x(1:150,4);
% T=x(1:150,5);
% I=(I2-0.1)./(7.9-0.1);
% H2=H2';
% [xf,PS] = mapminmax(H2);
% I2(:,1)=xf;
%
% H3=H3';
% [xf,PS2] = mapminmax(H3);
% I2(:,2)=xf;
%
% H4=H4';
% [xf,PS3] = mapminmax(H4);
% I2(:,3)=xf;
%
% H5=H5';
% [xf,PS4] = mapminmax(H5);
% I2(:,4)=xf;
% Thelp=T;
% T=T';
% [yf,PS5]= mapminmax(T);
% T=yf;
% T=T';
%
% for i=1:1:Runno
% [Fbest,Lbest,BestChart]=GSA(SearchAgents_no,Max_iteration,ElitistCheck,min_flag,Rpower,lb,ub,dim,fobj);
% BestSolutions1(i) = Fbest;
% [gBestScore,gBest,GlobalBestCost]= CPSOGSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
% BestSolutions4(i) = gBestScore;
% [BestSolACO,BestAnt,BestCostACO] = ACO(SearchAgents_no, Max_iteration,Q,tau0,alpha,rho,lb,ub,dim,fobj);
% BestSolutions5(i) = BestSolACO.Cost;
% [BestCost,Best_Hab,BestSol] = bbo( SearchAgents_no, Max_iteration,lb,ub,dim,fobj);
% BestSolutions6(i) = BestSol.Cost;
% [BestSolDE,DBestSol,BestCostDE] = DE(SearchAgents_no, Max_iteration,beta_min,beta_max,pCR,lb,ub,dim,fobj);
% BestSolutions7(i) = BestSolDE.Cost ;
% Rrate=0;
% W=Lbest(1:63);
% B=Lbest(64:75);
% W=gBest(1:63);
% B=gBest(64:75);
% W=BestAnt(1:63);
% B=BestAnt(64:75);
% W=Best_Hab(1:63);
% B=Best_Hab(64:75);
% W=DBestSol(1:63);
% B=DBestSol(64:75);
% for pp=1:150
% actualvalue=my_simulate(4,9,3,W,B,I2(pp,:));
% if(T(pp)==-1)
% if (actualvalue(1)>=0.95 && actualvalue(2)<0.05 && actualvalue(3)<0.05)
% Rrate=Rrate+1;
% end
% end
% if(T(pp)==0)
% if (actualvalue(1)<0.05 && actualvalue(2)>=0.95 && actualvalue(3)<0.05)
% Rrate=Rrate+1;
% end
% end
% if(T(pp)==1)
% if (actualvalue(1)<0.05 && actualvalue(2)<0.05 && actualvalue(3)>=0.95)
% Rrate=Rrate+1;
% end
% end
% end
% end
% end
%
% if Function_name=='F4'
%
% load Cancer.txt
% x=Cancer;
% %I2=x(1:150,1:4);
% H2=x(1:699,2:11);
% for iii=1:699
% for jjj=1:10
% H2(iii,jjj)=((H2(iii,jjj)-1)/9);
% end
% end
% I2=H2(1:699,1:9);
%
% T=H2(1:699,10);
% Hno=19;
% dim=11*19;
%
% for i=1:1:Runno
% Rrate=0;
% [Fbest,Lbest,BestChart]=GSA(SearchAgents_no,Max_iteration,ElitistCheck,min_flag,Rpower,lb,ub,dim,fobj);
% BestSolutions1(i) = Fbest;
% [gBestScore,gBest,GlobalBestCost]= CPSOGSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
% BestSolutions4(i) = gBestScore;
% [BestSolACO,BestAnt,BestCostACO] = ACO(SearchAgents_no, Max_iteration,Q,tau0,alpha,rho,lb,ub,dim,fobj);
% BestSolutions5(i) = BestSolACO.Cost;
% [BestCost,Best_Hab,BestSol] = bbo( SearchAgents_no, Max_iteration,lb,ub,dim,fobj);
% BestSolutions6(i) = BestSol.Cost;
% [BestSolDE,DBestSol,BestCostDE] = DE(SearchAgents_no, Max_iteration,beta_min,beta_max,pCR,lb,ub,dim,fobj);
% BestSolutions7(i) = BestSolDE.Cost ;
% W=Lbest(1:10*Hno);
% B=Lbest(10*Hno+1:dim);
% W=gBest(1:10*Hno);
% B=gBest(10*Hno+1:dim);
% W=BestAnt(1:10*Hno);
% B=BestAnt(10*Hno+1:dim);
% W=Best_Hab(1:10*Hno);
% B=Best_Hab(10*Hno+1:dim);
% W=DBestSol(1:10*Hno);
% B=DBestSol(10*Hno+1:dim);
% for pp=600:699
% actualvalue=my_simulate(9,Hno,1,W,B,I2(pp,:) );
% if(T(pp)>=0.3 && T(pp)<0.4)
% if (abs(actualvalue-0.333333333333333)<0.1)
% Rrate=Rrate+1;
% end
% end
% if(T(pp)>=0.1 && T(pp)<0.2)
% if (abs(actualvalue-0.111111111111111)<0.1)
% Rrate=Rrate+1;
% end
% end
%
% end
% end
% end
% if Function_name=='F5'
%
% load Heart.txt
% x=Heart;
% % I2=x(1:150,1:4);
% I2(:,1)=x(1:80,2);
% I2(:,2)=x(1:80,3);
% I2(:,3)=x(1:80,4);
% I2(:,4)=x(1:80,5);
% I2(:,5)=x(1:80,6);
% I2(:,6)=x(1:80,7);
% I2(:,7)=x(1:80,8);
% I2(:,8)=x(1:80,9);
% I2(:,9)=x(1:80,10);
% I2(:,10)=x(1:80,11);
% I2(:,11)=x(1:80,12);
% I2(:,12)=x(1:80,13);
% I2(:,13)=x(1:80,14);
% I2(:,14)=x(1:80,15);
% I2(:,15)=x(1:80,16);
% I2(:,16)=x(1:80,17);
% I2(:,17)=x(1:80,18);
% I2(:,18)=x(1:80,19);
% I2(:,19)=x(1:80,20);
% I2(:,20)=x(1:80,21);
% I2(:,21)=x(1:80,22);
% I2(:,22)=x(1:80,23);
% T=x(1:80,1);
%
% Hno=45;
% dim = 24*45+1;
%
% for i=1:1:Runno
%
% Rrate=0;
% [Fbest,Lbest,BestChart]=GSA(SearchAgents_no,Max_iteration,ElitistCheck,min_flag,Rpower,lb,ub,dim,fobj);
% BestSolutions1(i) = Fbest;
% [gBestScore,gBest,GlobalBestCost]= CPSOGSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
% BestSolutions4(i) = gBestScore;
% [BestSolACO,BestAnt,BestCostACO] = ACO(SearchAgents_no, Max_iteration,Q,tau0,alpha,rho,lb,ub,dim,fobj);
% BestSolutions5(i) = BestSolACO.Cost;
% [BestCost,Best_Hab,BestSol] = bbo( SearchAgents_no, Max_iteration,lb,ub,dim,fobj);
% BestSolutions6(i) = BestSol.Cost;
% [BestSolDE,DBestSol,BestCostDE] = DE(SearchAgents_no, Max_iteration,beta_min,beta_max,pCR,lb,ub,dim,fobj);
% BestSolutions7(i) = BestSolDE.Cost ;
% W=Lbest(1:23*Hno);
% B=Lbest(23*Hno+1:dim);
% W=gBest(1:23*Hno);
% B=gBest(23*Hno+1:dim);
% W=BestAnt(1:23*Hno);
% B=BestAnt(23*Hno+1:dim);
% W=Best_Hab(1:23*Hno);
% B=Best_Hab(23*Hno+1:dim);
% W=DBestSol(1:23*Hno);
% B=DBestSol(23*Hno+1:dim);
% for pp=1:80
% actualvalue=my_simulate(22,Hno,1,W,B,I2(pp,:) );
% if(T(pp)==1)
% if (actualvalue>=0.95)
% Rrate=Rrate+1;
% end
% end
% if(T(pp)==0)
% if (actualvalue(1)<0.05)
% Rrate=Rrate+1;
% end
% end
%
% end
% end
% end
% if Function_name=='F6' %% Sigmoid
%
% Hnode=15;
% dim = 3*Hnode+1;
%
% %for test 3 times more than the training samples
% % xf1=[0:0.01:pi];
% % yf1=sin(2.*xf1);
% % yf1=yf1.*exp(-xf1);
% xf1=[-3:0.05:3];
% % yf1=sin(2.*xf1);
% % yf1=yf1.*exp(-xf1);
% %yf1=xf1.^2;
% %yf1=xf1.^4-6.*xf1.^2+3;
% yf1=sigmf(xf1,[1 0]);
%
% % xf1=[-2*pi:0.05:2*pi];
% % yf1=sin(2.*xf1);
% %yf1=yf1.*exp(-xf1);
% yNN=zeros(1,10);
% [xf,PS] = mapminmax(xf1);
% [yf,PS2]= mapminmax(yf1);
% [M N]=size(xf);
% test_error=zeros(1,Runno);
% for i=1:1:Runno
% [Fbest,Lbest,BestChart]=GSA(SearchAgents_no,Max_iteration,ElitistCheck,min_flag,Rpower,lb,ub,dim,fobj);
% BestSolutions1(i) = Fbest;
% [gBestScore,gBest,GlobalBestCost]= CPSOGSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
% BestSolutions4(i) = gBestScore;
% [BestSolACO,BestAnt,BestCostACO] = ACO(SearchAgents_no, Max_iteration,Q,tau0,alpha,rho,lb,ub,dim,fobj);
% BestSolutions5(i) = BestSolACO.Cost;
% [BestCost,Best_Hab,BestSol] = bbo( SearchAgents_no, Max_iteration,lb,ub,dim,fobj);
% BestSolutions6(i) = BestSol.Cost;
% [BestSolDE,DBestSol,BestCostDE] = DE(SearchAgents_no, Max_iteration,beta_min,beta_max,pCR,lb,ub,dim,fobj);
% BestSolutions7(i) = BestSolDE.Cost ;
% W=1:2*Hnode;
% B=2*Hnode+1:3*Hnode+1;
% W=gBest(1:2*Hnode);
% B=gBest(2*Hnode+1:3*Hnode+1);
% W=BestAnt(1:2*Hnode);
% B=BestAnt(2*Hnode+1:3*Hnode+1);
% W=Best_Hab(1:2*Hnode);
% B=Best_Hab(2*Hnode+1:3*Hnode+1);
% W=DBestSol(1:2*Hnode);
% B=DBestSol(2*Hnode+1:3*Hnode+1);
%
% for pp=1:N
% yNN(pp)=my_simulate(1,15,1, W,B,xf(pp));
% end
%
% disp(['GSA is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(Fbest),')'])
% disp(['CPSOGSA is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(gBestScore), ')'])
% disp(['ACO is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(BestSolACO.Cost),')'])
% disp(['BBO is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(BestSol.Cost),')'])
% disp(['DE is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(BestSolDE.Cost),')'])
% end
% [M N]=size(xf);
% test_error=zeros(1,Runno);
% for i=1:1:Runno
% % [Fbest,Lbest,BestChart]=GSA(SearchAgents_no,Max_iteration,ElitistCheck,min_flag,Rpower,lb,ub,dim,fobj);
% % BestSolutions1(i) = Fbest;
% % [gBestScore,gBest,GlobalBestCost]= CPSOGSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
% % BestSolutions4(i) = gBestScore;
% % [BestSolACO,BestAnt,BestCostACO] = ACO(SearchAgents_no, Max_iteration,Q,tau0,alpha,rho,lb,ub,dim,fobj);
% % BestSolutions5(i) = BestSolACO.Cost;
% % [BestCost,Best_Hab,BestSol] = bbo( SearchAgents_no, Max_iteration,lb,ub,dim,fobj);
% % BestSolutions6(i) = BestSol.Cost;
% [BestSolDE,DBestSol,BestCostDE] = DE(SearchAgents_no, Max_iteration,beta_min,beta_max,pCR,lb,ub,dim,fobj);
% BestSolutions(i) = BestSolDE.Cost ;
% %
% W=Lbest(1:2*Hnode);
% B= Lbest(2*Hnode+1:3*Hnode+1);
% W=gBest(1:2*Hnode);
% B=gBest(2*Hnode+1:3*Hnode+1);
% W=BestAnt(1:2*Hnode);
% B=BestAnt(2*Hnode+1:3*Hnode+1);
% W=Best_Hab(1:2*Hnode);
% B=Best_Hab(2*Hnode+1:3*Hnode+1);
% W=DBestSol(1:2*Hnode);
% B=DBestSol(2*Hnode+1:3*Hnode+1);
%
% for pp=1:N
% yNN(pp)=my_simulate(1,15,1, W,B,xf(pp));
% end
% %
% disp(['GSA is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(Fbest),')'])
% disp(['CPSOGSA is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(gBestScore), ')'])
% disp(['ACO is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(BestSolACO.Cost),')'])
% disp(['BBO is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(BestSol.Cost),')'])
% disp(['DE is training FNN (Run # = ', num2str(i),' ,MSE = ', num2str(BestSolDE.Cost),')'])
% end
% end
% % A_Classification_rate=mean(Classification_rate);
% Average= mean(BestSolutions);
% StandDP=std(BestSolutions);
% Med = median(BestSolutions);
% [BestValueP I] = min(BestSolutions);
% [WorstValueP IM]=max(BestSolutions);
% end
% figure
% set(axes,'FontName','Times New Roman');
% hold on
% grid on;
% xfDenorm = mapminmax('reverse',xf,PS);
% yfDenorm = mapminmax('reverse',yNN,PS2);
% test_error(1,i)=test_error(1,i)+sum(abs( yfDenorm- yf1 ));
% A_Test_Error=mean(test_error);
% plot(xf1,yf1,'DisplayName','Real curve','Color','b');
% plot(xfDenorm,yfDenorm,'DisplayName','Approximated curve','Marker','.','LineStyle','-','Color','r');
% %eqtext = '$$sin(2x)e^{-x}$$';
%
% name='GSA'
%
% title([['\fontsize{12}\it ', name]],'FontName','Times New Roman');
% xlabel('\fontsize{12}\it X');
% ylabel('\fontsize{12}\it Y');
% legend('toggle');
% set(legend,'FontAngle','italic','FontName','Times New Roman')
disp(['Best=',num2str( BestValueP)])
disp(['Worst=',num2str(WorstValueP)])
disp(['Average=',num2str( Average)])
disp(['Standard_Deviation=',num2str( StandDP)])
disp(['Median=',num2str(Med)])
% % disp(['Mean_Test_Error = ' , num2str(A_Test_Error)])
figure
semilogy(1:Max_iteration,BestChart,'DisplayName','GSA','Color','g','Marker','o','LineStyle','-','LineWidth',2,...
'MarkerEdgeColor','g','MarkerFaceColor',[.49 1 .63],'MarkerSize',5);
hold on
semilogy(1:Max_iteration,GlobalBestCost,'DisplayName','CPSOGSA', 'Color', 'r','Marker','diamond','LineStyle','-','LineWidth',2,...
'MarkerEdgeColor','r','MarkerFaceColor',[.49 1 .63],'MarkerSize',5);
semilogy(1:Max_iteration,BestCostACO,'DisplayName','ACO','Color','c','Marker','square','LineStyle','-','LineWidth',2,...
'MarkerEdgeColor','c','MarkerFaceColor',[.49 1 .63],'MarkerSize',5);
% semilogy(1:Max_iteration,BestCost,'DisplayName','BBO','Color','b','Marker','*','LineStyle','-','LineWidth',2,...
% 'MarkerEdgeColor','b','MarkerFaceColor',[.49 1 .63],'MarkerSize',5);
semilogy(1:Max_iteration,BestCostDE,'DisplayName','DE','Color','y','Marker','+','LineStyle','-','LineWidth',2,...
'MarkerEdgeColor','y','MarkerFaceColor',[.49 1 .63],'MarkerSize',5);
title ('\fontsize{12}\bf XOR Dataset');
% title ('\fontsize{12}\bf Baloon Dataset');
% title ('\fontsize{12}\bf Iris Dataset');
% title ('\fontsize{12}\bf Cancer Dataset');
% title ('\fontsize{12}\bf Heart Dataset');
% title ('\fontsize{12}\bf Sigmoid Dataset');
% title ('\fontsize{12}\bf Cosine Dataset');
% title ('\fontsize{12}\bf Sine Dataset');
xlabel('\fontsize{12}\bf Iteration');
ylabel('\fontsize{12}\bf log(MSE)');
legend('\fontsize{10}\bf GSA','\fontsize{10}\bf CPSOGSA','\fontsize{10}\bf ACO','\fontsize{10}\bf BBO','\fontsize{10}\bf DE',1);
axis tight
box on
3 仿真结果
4 参考文献
[1]陈国福, 陈小山, 张瑞. 基于引力搜索和粒子群混合优化算法的证券投资组合问题研究[J]. 运筹与管理, 2018, 27(9):6.
部分理论引用网络文献，若有侵权联系博主删除。
5 MATLAB代码与数据下载地址
见博客主页
