【智能优化算法】基于自适应灰狼优化算法求解单目标优化问题附matlab代码

%___________________________________________________________________% % Grey Wold Optimizer (GWO) source codes version 1.0 % % % % Developed in MATLAB R2011b(7.13) % % % % Author and programmer: Seyedali Mirjalili % % % % e-Mail: % % seyedali.mirjalili@griffithuni.edu.au % % % % Homepage: http://www.alimirjalili.com % % % % Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis % % Grey Wolf Optimizer, Advances in Engineering % % Software , in press, % % DOI: 10.1016/j.advengsoft.2013.12.007 % % % %___________________________________________________________________% % Grey Wolf Optimizer function [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj) % initialize alpha, beta, and delta_pos 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 Delta_pos = zeros(1,dim); Delta_score = inf; %change this to -inf for maximization problems %Initialize the positions of search agents Positions = initialization(SearchAgents_no,dim,ub,lb); Convergence_curve = zeros(1,Max_iter); l = 0;% Loop counter % Main loop while l<Max_iter for i=1:size(Positions,1) % Return back the search agents that go beyond the boundaries of the search space Flag4ub = Positions(i,:)>ub; Flag4lb = Positions(i,:)<lb; Positions(i, : )=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb; % Calculate objective function for each search agent fitness = fobj(Positions(i,:)); % Update Alpha, Beta, and Delta if fitness<Alpha_score Alpha_score = fitness; % Update alpha Alpha_pos = Positions(i,:); end if fitness>Alpha_score && fitness<Beta_score Beta_score = fitness; % Update beta Beta_pos = Positions(i,:); end if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score Delta_score = fitness; % Update delta Delta_pos = Positions(i,:); end end a = 2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0 % 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 (3.3) C1 = 2*r2; % Equation (3.4) D_alpha = abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1 X1 = Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1 r1 = rand(); r2 = rand(); A2 = 2*a*r1-a; % Equation (3.3) C2 = 2*r2; % Equation (3.4) D_beta = abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2 X2 = Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2 r1 = rand(); r2 = rand(); A3 = 2*a*r1-a; % Equation (3.3) C3 = 2*r2; % Equation (3.4) D_delta = abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3 X3 = Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3 Positions(i,j) = (X1+X2+X3)/3;% Equation (3.7) end end l = l+1; Convergence_curve(l) = Alpha_score; end