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GWO.m

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+%  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,Value)
+
+% 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','b')
+        xlabel('Iteration');
+        ylabel('Best score obtained so far');        
+        drawnow
+    end
+
+
+    set(handles.itertext,'String', ['The current iteration is ', num2str(l)])
+    set(handles.optimumtext,'String', ['The current optimal value is ', num2str(Alpha_score)])
+    if Value==1
+        hold on
+        scatter(l*ones(1,SearchAgents_no),All_fitness,'.','k')
+    end
+end
+
+
+