基于灰狼优化和布谷鸟搜索混合搜索算法matlab仿真+仿真录像

function [Alpha_score,Alpha_pos,Convergence_curve]=AGWO_CS(SearchAgents_no,Max_iter,lb,ub,dim,fobj) Alpha_pos=zeros(1,dim); Alpha_score=inf; %change this to -inf for maximization problems Beta_pos=zeros(1,dim); Beta_score=inf; %change this to -inf for maximization problems %Initialize the positions of search agents Positions=initialization(SearchAgents_no,dim,ub,lb); Positions=sort(Positions); oldPositions=Positions; Convergence_curve=zeros(1,Max_iter); l=0;% Loop counter % Main loop while l<Max_iter for i=1:size(Positions,1) fitness(i)=fobj(Positions(i,:)); % Update Alpha, Beta, and Delta if fitness(i)<Alpha_score Alpha_score=fitness(i); % Update alpha Alpha_pos=Positions(i,:); end if fitness(i)>Alpha_score && fitness(i)<Beta_score Beta_score=fitness(i); % Update beta Beta_pos=Positions(i,:); end end a=2-(cos(rand())*l/Max_iter); % a decreases non-linearly fron 2 to 1. % Update the Position of search agents including omegas for i=1:size(Positions,1) for j=1:size(Positions,2) r1=rand(); % r1 is a random number in [0,1] r2=rand(); % r2 is a random number in [0,1] A1=2*a*r1-a; % Equation (4) C1=2*r2; % Equation (5) D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (6)-part 1 X1(i,j)=Alpha_pos(j)-A1*D_alpha; % Equation (7)-part 1 r1=rand(); r2=rand(); A2=2*a*r1-a; % Equation (4) C2=2*r2; % Equation (5) D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (6)-part 2 X2(i,j)=Beta_pos(j)-A2*D_beta; % Equation (7)-part 2 end end [~,index]=min(fitness); best=Positions(index,:); X1=get_cuckoos(X1,best,lb,ub); X2=get_cuckoos(X2,best,lb,ub); %% control is sent back to AGWO Positions=(X1+X2)/2;% Equation (8) % Positions=sort(Positions); if Positions(i,j)>ub Positions(i,j)=ub; elseif Positions(i,j)<lb Positions(i,j)=lb; end l=l+1; Convergence_curve(l)=Alpha_score; end end