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Author: alimirjalili

GWO.m

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+%___________________________________________________________________%
+%  Grey Wolf Optimizer (GWO) source codes version 1.0               %
+%                                                                   %
+%  Developed in MATLAB R2011b(7.13)                                 %
+%                                                                   %
+%  Author and programmer: Seyedali Mirjalili                        %
+%                                                                   %
+%         e-Mail: [email protected]                           %
+%                 [email protected]             %
+%                                                                   %
+%       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
+
+
+