蜻蜓算法DA(DragonflyAlgorithm)附matlab代码.zip

%___________________________________________________________________% % Multi-Objective Dragonfly Algorithm (MODA) source codes demo % % version 1.0 % 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 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Initial parameters of the MODA algorithm max_iter=100; N=100; ArchiveMaxSize=100; Archive_X=zeros(100,dim); Archive_F=ones(100,obj_no)*inf; Archive_member_no=0; r=(ub-lb)/2; V_max=(ub(1)-lb(1))/10; Food_fitness=inf*ones(1,obj_no); Food_pos=zeros(dim,1); Enemy_fitness=-inf*ones(1,obj_no); Enemy_pos=zeros(dim,1); X=initialization(N,dim,ub,lb); fitness=zeros(N,2); DeltaX=initialization(N,dim,ub,lb); iter=0; position_history=zeros(N,max_iter,dim); for iter=1:max_iter r=(ub-lb)/4+((ub-lb)*(iter/max_iter)*2); w=0.9-iter*((0.9-0.2)/max_iter); my_c=0.1-iter*((0.1-0)/(max_iter/2)); if my_c<0 my_c=0; end if iter<(3*max_iter/4) s=my_c; % Seperation weight a=my_c; % Alignment weight c=my_c; % Cohesion weight f=2*rand; % Food attraction weight e=my_c; % Enemy distraction weight else s=my_c/iter; % Seperation weight a=my_c/iter; % Alignment weight c=my_c/iter; % Cohesion weight f=2*rand; % Food attraction weight e=my_c/iter; % Enemy distraction weight end for i=1:N %Calculate all the objective values first Particles_F(i,:)=ObjectiveFunction(X(:,i)'); if dominates(Particles_F(i,:),Food_fitness) Food_fitness=Particles_F(i,:); Food_pos=X(:,i); end if dominates(Enemy_fitness,Particles_F(i,:)) if all(X(:,i)<ub') && all( X(:,i)>lb') Enemy_fitness=Particles_F(i,:); Enemy_pos=X(:,i); end end end [Archive_X, Archive_F, Archive_member_no]=UpdateArchive(Archive_X, Archive_F, X, Particles_F, 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); % Chose the archive member in the least population area as foods % to improve coverage index=RouletteWheelSelection(1./Archive_mem_ranks); if index==-1 index=1; end Food_fitness=Archive_F(index,:); Food_pos=Archive_X(index,:)'; % Chose the archive member in the most population area as enemies % to improve coverage index=RouletteWheelSelection(Archive_mem_ranks); if index==-1 index=1; end Enemy_fitness=Archive_F(index,:); Enemy_pos=Archive_X(index,:)'; for i=1:N index=0; neighbours_no=0; clear Neighbours_V clear Neighbours_X % Find the neighbouring solutions for j=1:N Dist=distance(X(:,i),X(:,j)); if (all(Dist<=r) && all(Dist~=0)) index=index+1; neighbours_no=neighbours_no+1; Neighbours_V(:,index)=DeltaX(:,j); Neighbours_X(:,index)=X(:,j); end end % Seperation%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.1) S=zeros(dim,1); if neighbours_no>1 for k=1:neighbours_no S=S+(Neighbours_X(:,k)-X(:,i)); end S=-S; else S=zeros(dim,1); end % Alignment%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.2) if neighbours_no>1 A=(sum(Neighbours_V')')/neighbours_no; else A=DeltaX(:,i); end % Cohesion%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.3) if neighbours_no>1 C_temp=(sum(Neighbours_X')')/neighbours_no; else C_temp=X(:,i); end C=C_temp-X(:,i); % Attraction to food%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.4) Dist2Attraction=distance(X(:,i),Food_pos(:,1)); if all(Dist2Attraction<=r) F=Food_pos-X(:,i); iter; else F=0; end % Distraction from enemy%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.5) Dist=distance(X(:,i),Enemy_pos(:,1)); if all(Dist<=r) E=Enemy_pos+X(:,i); else E=zeros(dim,1); end for tt=1:dim if X(tt,i)>ub(tt) X(tt,i)=lb(tt); DeltaX(tt,i)=rand; end if X(tt,i)<lb(tt) X(tt,i)=ub(tt); DeltaX(tt,i)=rand; end end if any(Dist2Attraction>r) if neighbours_no>1 for j=1:dim DeltaX(j,i)=w*DeltaX(j,i)+rand*A(j,1)+rand*C(j,1)+rand*S(j,1); if DeltaX(j,i)>V_max DeltaX(j,i)=V_max; end if DeltaX(j,i)<-V_max DeltaX(j,i)=-V_max; end X(j,i)=X(j,i)+DeltaX(j,i); end else X(:,i)=X(:,i)+Levy(dim)'.*X(:,i); DeltaX(:,i)=0; end else for j=1:dim DeltaX(j,i)=s*S(j,1)+a*A(j,1)+c*C(j,1)+f*F(j,1)+e*E(j,1) + w*DeltaX(j,i); if DeltaX(j,i)>V_max DeltaX(j,i)=V_max; end if DeltaX(j,i)<-V_max DeltaX(j,i)=-V_max; end X(j,i)=X(j,i)+DeltaX(j,i); end end Flag4ub=X(:,i)>ub'; Flag4lb=X(:,i)<lb'; X(:,i)=(X(:,i).*(~(Flag4ub+Flag4lb)))+ub'.*Flag4ub+lb'.*Flag4lb; end display(['At the iteration ', num2str(iter), ' there are ', num2str(Archive_member_no), ' non-dominated solutions in the archive']); end figure Draw_ZDT1(); hold on if obj_no==2 plot(Archive_F(:,1),Archive_F(:,2),'ko','MarkerSize',8,'markerfacecolor','k'); else plot3(Archive_F(:,1),Archive_F(:,2),Archive_F(:,3),'ko','MarkerSize',8,'markerfacecolor','k'); end legend('True PF','Obtained PF'); title('MODA');