matlab-基于matlab的BOA蝴蝶优化算法仿真,测试20多个目标函数-源码

% function [fmin,best_pos,Convergence_curve]=BOA(n,N_iter,Lb,Ub,dim,fobj) p=0.8; % probabibility switch power_exponent=0.1; sensory_modality=0.01; %Initialize the positions of search agents Sol=initialization(n,dim,Ub,Lb); for i=1:n, Fitness(i)=fobj(Sol(i,:)); end % Find the current best_pos [fmin,I]=min(Fitness); best_pos=Sol(I,:); S=Sol; % Start the iterations -- Butterfly Optimization Algorithm for t=1:N_iter, for i=1:n, % Loop over all butterflies/solutions %Calculate fragrance of each butterfly which is correlated with objective function Fnew=fobj(S(i,:)); FP=(sensory_modality*(Fnew^power_exponent)); %Global or local search if rand<p, dis = rand * rand * best_pos - Sol(i,:); %Eq. (2) in paper S(i,:)=Sol(i,:)+dis*FP; else % Find random butterflies in the neighbourhood epsilon=rand; JK=randperm(n); dis=epsilon*epsilon*Sol(JK(1),:)-Sol(JK(2),:); S(i,:)=Sol(i,:)+dis*FP; %Eq. (3) in paper end % Check if the simple limits/bounds are OK S(i,:)=simplebounds(S(i,:),Lb,Ub); % Evaluate new solutions Fnew=fobj(S(i,:)); %Fnew represents new fitness values % If fitness improves (better solutions found), update then if (Fnew<=Fitness(i)), Sol(i,:)=S(i,:); Fitness(i)=Fnew; end % Update the current global best_pos if Fnew<=fmin, best_pos=S(i,:); fmin=Fnew; end end Convergence_curve(t,1)=fmin; %Update sensory_modality sensory_modality=sensory_modality_NEW(sensory_modality, N_iter); end % Boundary constraints function s=simplebounds(s,Lb,Ub) % Apply the lower bound ns_tmp=s; I=ns_tmp<Lb; ns_tmp(I)=Lb; % Apply the upper bounds J=ns_tmp>Ub; ns_tmp(J)=Ub; % Update this new move s=ns_tmp; function y=sensory_modality_NEW(x,Ngen) y=x+(0.025/(x*Ngen));