gusucode.com > Toolbox_all_algorithms > Toolbox_all_algorithms/GWO/GWO.m
% Grey Wold Optimizer (GWO) source codes version 1.1 % % % % 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/GWO.html % % % % Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis % % Grey Wolf Optimizer, Advances in Engineering % % Software, Volume 69, March 2014, Pages 46-61, % % http://dx.doi.org/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,handles) % 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) % Calculate objective function for each search agent fitness=fobj(Positions(i,:)); All_fitness(1,i)=fitness; % 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 % 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; end l=l+1; Convergence_curve(l)=Alpha_score; if l>1 line([l-1 l], [Convergence_curve(l-1) Convergence_curve(l)],'Color',[0.9290 0.6940 0.1250]) xlabel('Iteration'); ylabel('Best score obtained so far'); drawnow end results = get(handles.uitable1,'data'); results{1,1}=l; results{1,2}=Alpha_score; set(handles.uitable1,'data',results); end