多目标蝗虫优化算法（MOGOA）【含Matlab源码 937期】.zip

%_________________________________________________________________________________ % Multi-objective Grasshopper Optimization Algorithm (MOGOA) source codes version 1.0 % % Developed in MATLAB R2016a % _______________________________________________________________________ clc; clear; close all; % Change these details with respect to your problem%%%%%%%%%%%%%% ObjectiveFunction=@ZDT1; dim=5; lb=0; ub=1; obj_no=2; if size(ub,2)==1 ub=ones(1,dim)*ub; lb=ones(1,dim)*lb; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% flag=0; if (rem(dim,2)~=0) dim = dim+1; ub = [ub, 1]; lb = [lb, 0]; flag=1; end max_iter=100; N=200; ArchiveMaxSize=100; Archive_X=zeros(100,dim); Archive_F=ones(100,obj_no)*inf; Archive_member_no=0; %Initialize the positions of artificial whales GrassHopperPositions=initialization(N,dim,ub,lb); TargetPosition=zeros(dim,1); TargetFitness=inf*ones(1,obj_no); cMax=1; cMin=0.00004; %calculate the fitness of initial grasshoppers for iter=1:max_iter for i=1:N Flag4ub=GrassHopperPositions(:,i)>ub'; Flag4lb=GrassHopperPositions(:,i)<lb'; GrassHopperPositions(:,i)=(GrassHopperPositions(:,i).*(~(Flag4ub+Flag4lb)))+ub'.*Flag4ub+lb'.*Flag4lb; GrassHopperFitness(i,:)=ObjectiveFunction(GrassHopperPositions(:,i)'); if dominates(GrassHopperFitness(i,:),TargetFitness) TargetFitness=GrassHopperFitness(i,:); TargetPosition=GrassHopperPositions(:,i); end end [Archive_X, Archive_F, Archive_member_no]=UpdateArchive(Archive_X, Archive_F, GrassHopperPositions, GrassHopperFitness, Archive_member_no); if Archive_member_no>ArchiveMaxSize Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no); [Archive_X, Archive_F, Archive_mem_ranks, Archive_member_no]=HandleFullArchive(Archive_X, Archive_F, Archive_member_no, Archive_mem_ranks, ArchiveMaxSize); else Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no); end Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no); index=RouletteWheelSelection(1./Archive_mem_ranks); if index==-1 index=1; end TargetFitness=Archive_F(index,:); TargetPosition=Archive_X(index,:)'; c=cMax-iter*((cMax-cMin)/max_iter); % Eq. (3.8) in the paper for i=1:N temp= GrassHopperPositions; for k=1:2:dim S_i=zeros(2,1); for j=1:N if i~=j Dist=distance(temp(k:k+1,j), temp(k:k+1,i)); r_ij_vec=(temp(k:k+1,j)-temp(k:k+1,i))/(Dist+eps); xj_xi=2+rem(Dist,2); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.2) in the paper s_ij=((ub(k:k+1)' - lb(k:k+1)') .*c/2)*S_func(xj_xi).*r_ij_vec; S_i=S_i+s_ij; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% end end S_i_total(k:k+1, :) = S_i; end X_new=c*S_i_total'+(TargetPosition)'; % Eq. (3.7) in the paper GrassHopperPositions_temp(i,:)=X_new'; end % GrassHopperPositions GrassHopperPositions=GrassHopperPositions_temp'; display(['At the iteration ', num2str(iter), ' there are ', num2str(Archive_member_no), ' non-dominated solutions in the archive']); end if (flag==1) TargetPosition = TargetPosition(1:dim-1); end figure Draw_ZDT1(); hold on plot(Archive_F(:,1),Archive_F(:,2),'ro','MarkerSize',8,'markerfacecolor','k'); legend('True PF','Obtained PF'); title('MOGOA'); set(gcf, 'pos', [403 466 230 200])