BDA1.zip_Dragonfly_Dragonfly algorithm_最新算法_蜻蜓算法_蜻蜓算法程序

%___________________________________________________________________% % Binary Dragonfly Algorithm (BDA) source codes demo version 1.0 % % % % Developed in MATLAB R2011b(7.13) % % % % Author and programmer: Seyedali Mirjalili % % % % e-Mail: ali.mirjalili@gmail.com % % seyedali.mirjalili@griffithuni.edu.au % % % % Homepage: http://www.alimirjalili.com % % % % Main paper: % % % % S. Mirjalili, Dragonfly algorithm: a new meta-heuristic % % optimization technique for solving single-objective, discrete, % % and multi-objective problems, Neural Computing and Applications % % DOI: http://dx.doi.org/10.1007/s00521-015-1920-1 % %___________________________________________________________________% function [Best_pos, Best_score ,Convergence_curve]=BDA(N, max_iter, nVar, CostFunction) display('The BDA algorithm is optimizing your problem...') dim=nVar; Food_fitness=inf; Food_pos=zeros(dim,1); Enemy_fitness=-inf; Enemy_pos=zeros(dim,1); % Initialize X and DeltaX vectors for i=1:N, for j=1:nVar if rand<=0.5 X(j,i)=0; else X(j,i)=1; end if rand<=0.5 DeltaX(j,i)=0; else DeltaX(j,i)=1; end end end Fitness=zeros(1,N); for iter=1:max_iter w=0.9-iter*((0.9-0.4)/max_iter); my_c=0.1-iter*((0.1-0)/(max_iter/2)); if my_c<0 my_c=0; end s=2*rand*my_c; % Seperation weight a=2*rand*my_c; % Alignment weight c=2*rand*my_c; % Cohesion weight f=2*rand; % Food attraction weight e=my_c; % Enemy distraction weight if iter>(3*max_iter/4) e=0; end for i=1:N %Calculate all the objective values first Fitness(1,i)=CostFunction(X(:,i)'); if Fitness(1,i)<Food_fitness Food_fitness=Fitness(1,i); Food_pos=X(:,i); end if Fitness(1,i)>Enemy_fitness Enemy_fitness=Fitness(1,i); Enemy_pos=X(:,i); end end for i=1:N index=0; neighbours_no=0; clear Neighbours_DeltaX clear Neighbours_X %Find the neighbouring solutions (all the dragonflies are assumed as a group in binary seach spaces) for j=1:N if (i~=j) index=index+1; neighbours_no=neighbours_no+1; Neighbours_DeltaX(:,index)=DeltaX(:,j); Neighbours_X(:,index)=X(:,j); end end % Seperation%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.1) S=zeros(dim,1); for k=1:neighbours_no S=S+(Neighbours_X(:,k)-X(:,i)); end S=-S; % Alignment%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.2) A=(sum(Neighbours_DeltaX')')/neighbours_no; % Cohesion%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.3) C_temp=(sum(Neighbours_X')')/neighbours_no; C=C_temp-X(:,i); % Attraction to food%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.4) F=Food_pos-X(:,i); % Distraction from enemy%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Eq. (3.5) E=Enemy_pos+X(:,i); for j=1:dim % Eq. (3.6) 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)>6 DeltaX(j,i)=6; end if DeltaX(j,i)<-6 DeltaX(j,i)=-6; end % Eq. (3.11) T=abs(DeltaX(j,i)/sqrt((1+DeltaX(j,i)^2))); %V3 transfer function % Eq. (3.12) if rand<T %Equation (10) X(j,i)=~X(j,i); end end end Convergence_curve(iter)=Food_fitness; Best_pos=Food_pos; Best_score=Food_fitness; end