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% Whale Optimization Algorithm (WOA) source codes demo 1.0 %
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% Developed in MATLAB R2011b(7.13) %
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% Author and programmer: Seyedali Mirjalili %
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% e-Mail: ali.mirjalili@gmail.com %
% seyedali.mirjalili@griffithuni.edu.au %
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% Homepage: http://www.alimirjalili.com %
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% Main paper: S. Mirjalili, A. Lewis %
% The Whale Optimization Algorithm, %
% Advances in Engineering Software , in press, %
% DOI: http://dx.doi.org/10.1016/j.advengsoft.2016.01.008 %
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%_________________________________________________________________________%
% The Whale Optimization Algorithm
function [Best_Cost,Best_pos,curve]=WOA(pop,Max_iter,lb,ub,dim,fobj)
% initialize position vector and score for the leader
Best_pos=zeros(1,dim);
Best_Cost=inf; %change this to -inf for maximization problems
%Initialize the positions of search agents
Positions=initialization(pop,dim,ub,lb);
curve=zeros(1,Max_iter);
t=0;% Loop counter
% Main loop
while t<Max_iter
t
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 the leader
if fitness<Best_Cost % Change this to > for maximization problem
Best_Cost=fitness; % Update alpha
Best_pos=Positions(i,:);
end
end
a=2-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3)
% a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)
a2=-1+t*((-1)/Max_iter);
% Update the Position of search agents
for i=1:size(Positions,1)
r1=rand(); % r1 is a random number in [0,1]
r2=rand(); % r2 is a random number in [0,1]
A=2*a*r1-a; % Eq. (2.3) in the paper
C=2*r2; % Eq. (2.4) in the paper
b=1; % parameters in Eq. (2.5)
l=(a2-1)*rand+1; % parameters in Eq. (2.5)
p = rand(); % p in Eq. (2.6)
for j=1:size(Positions,2)
if p<0.5
if abs(A)>=1
rand_leader_index = floor(pop*rand()+1);
X_rand = Positions(rand_leader_index, :);
D_X_rand=abs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7)
Positions(i,j)=X_rand(j)-A*D_X_rand; % Eq. (2.8)
elseif abs(A)<1
D_Leader=abs(C*Best_pos(j)-Positions(i,j)); % Eq. (2.1)
Positions(i,j)=Best_pos(j)-A*D_Leader; % Eq. (2.2)
end
elseif p>=0.5
distance2Leader=abs(Best_pos(j)-Positions(i,j));
% Eq. (2.5)
Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Best_pos(j);
end
end
end
t=t+1;
curve(t)=Best_Cost;
[t Best_Cost];
end
