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
clearallcloseallclcQ=1;% ACO Parametertau0=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 datasetsFunction_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%ifFunction_name=='F1'input= [00001111;00110011;01010101];target3=[01101001];Hno=7;dim=5*7+1; % Dimension of the problemfori=1:1:RunnoRrate=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);forpp=1:8actualvalue=my_simulate(3,Hno,1,W,B,input(:,pp)');if(target3(pp)==1)if(actualvalue>=0.95)Rrate=Rrate+1;endendif(target3(pp)==0)if(actualvalue(1)<0.05)Rrate=Rrate+1;endendendClassification_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)),')'])endA_Classification_rate=mean(Classification_rate);Average=mean(BestSolutions1);StandDP=std(BestSolutions1);Med=median(BestSolutions1);[BestValuePI] =min(BestSolutions1);[WorstValuePIM]=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)])figuresemilogy(1:Max_iteration,BestChart,'DisplayName','GSA','Color','g','Marker','o','LineStyle','-','LineWidth',2,...'MarkerEdgeColor','g','MarkerFaceColor',[.491.63],'MarkerSize',5);holdonsemilogy(1:Max_iteration,GlobalBestCost,'DisplayName','CPSOGSA','Color','r','Marker','diamond','LineStyle','-','LineWidth',2,...'MarkerEdgeColor','r','MarkerFaceColor',[.491.63],'MarkerSize',5);semilogy(1:Max_iteration,BestCostACO,'DisplayName','ACO','Color','c','Marker','square','LineStyle','-','LineWidth',2,...'MarkerEdgeColor','c','MarkerFaceColor',[.491.63],'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',[.491.63],'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);axistightboxon
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, , 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
