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JOS (Joint Opposite Selection)
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JOS: A well-match of Selective Leading Opposition (SLO) and Dynamic Opposite (DO)
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@FlorentinaJOS
FlorentinaJOS committed Jan 4, 2022
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254 changes: 254 additions & 0 deletions GWO_JOS.m
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function [Alpha_score,Alpha_pos,GWOJOS]=GWO_JOS(N,T,maxRun,maxFE,BFid,nD,fhd,Jr)

if nargin ~= 8
N=30; % Population size
maxRun = 10; % Maximum Run
BFid = 1; % Number id of benchmark function
nD = 10; % Number of dimensions
maxFE = 10000*nD; % Number of function evaluations
Jr=0.25; % Jumping Rate
T=ceil(maxFE/N); % Maximum number of iterations
fhd=str2func('cec17_func');
end

lb = -100*ones(1,nD); ub = 100*ones(1,nD);
dim = nD;

disp('GWO-JOS is now tackling your problem')

GWOJOS=zeros(maxRun+1,T);

for run=1:maxRun
tic
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%initializing boundary for opposition
Boundary_no= size(ub,2); % numnber of boundaries

% If the boundaries of all variables are equal and user enter a signle
% number for both ub and lb
if Boundary_no==1
for i=1:dim
upper(1,i)=ub;
lower(1,i)=lb;
end
% If each variable has a different lb and ub
else
for i=1:dim
upper(1,i)=ub(i);
lower(1,i)=lb(i);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% 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
X=initialization(N,dim,ub,lb);

for i=1:N
OP(i,:)=((ub-lb).*rand(size(lb)))+lb-X(i,:);
RO(i,:) = rand*OP(i,:);
DO(i,:) = X(i,:) + rand*(RO(i,:)-X(i,:));

for j=1:dim
if DO(i,j)<lb(1,j)
DO(i,j)=lb(1,j);
end
if DO(i,j)>ub(1,j)
DO(i,j)=ub(1,j);
end
end
end
X=DO;

t=0;% Loop counter
nFE=0;
Row_Id=1;
while nFE<maxFE
for i=1:size(X,1)

% Return back the search agents that go beyond the boundaries of the search space
Flag4ub=X(i,:)>ub;
Flag4lb=X(i,:)<lb;
X(i,:)=(X(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;

% Calculate objective function for each search agent
fitness(i)=feval(fhd,X(i,:)',BFid);
nFE = nFE + 1;
if nFE > maxFE; break; end

% Update Alpha, Beta, and Delta
if fitness(i)<Alpha_score
Alpha_score=fitness(i); % Update alpha
Alpha_pos=X(i,:);
Row_Id=i;
end

if fitness(i)>Alpha_score && fitness(i)<Beta_score
Beta_score=fitness(i); % Update beta
Beta_pos=X(i,:);
Row_Id=i;
end

if fitness(i)>Alpha_score && fitness(i)>Beta_score && fitness(i)<Delta_score
Delta_score=fitness(i); % Update delta
Delta_pos=X(i,:);
Row_Id=i;
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%updating boundary for opposition after every iteration
for x=1:size(X,1)
for y=1:size(X,2)
if upper(1,y)<X(x,y)
upper(1,y)=X(x,y);
end
if lower(1,y)>X(x,y)
lower(1,y)=X(x,y);
end
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
end
t=t+1;
GWOJOS(1,t) = nFE; %nFE
GWOJOS(run+1,t) = Alpha_score;

a=2-t*((2)/T); % a decreases linearly fron 2 to 0
threshold=a;
X=corOppose2(X,ub,lb,upper,lower,dim,threshold,Row_Id);

% Update the Position of search agents including omegas
for i=1:size(X,1)
for j=1:size(X,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)-X(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)-X(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)-X(i,j)); % Equation (3.5)-part 3
X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3

X(i,j)=(X1+X2+X3)/3;% Equation (3.7)

end
end

if rand < Jr && nFE+N < maxFE

for i=1:N
OP(i,:)=((ub-lb).*rand(size(lb)))+lb-X(i,:);
RO(i,:) = rand*OP(i,:);
DO(i,:) = X(i,:) + rand*(RO(i,:)-X(i,:));
for j=1:dim
if DO(i,j)<lb(1,j)
DO(i,j)=lb(1,j);
end
if DO(i,j)>ub(1,j)
DO(i,j)=ub(1,j);
end
end
end
X=DO;
end
end
toc
end
% display(['The best solution obtained by GWO is : ', num2str(Best_pos)]);
% display(['The best optimal value of the objective funciton found by GWO is : ', num2str(Best_score)]);
end

function Positions=initialization(N,dim,ub,lb)
Boundary_no= size(ub,2); % numnber of boundaries

% If the boundaries of all variables are equal and user enter a signle
% number for both ub and lb
if Boundary_no==1
Positions=rand(N,dim).*(ub-lb)+lb;
end

% If each variable has a different lb and ub
if Boundary_no>1
for i=1:dim
ub_i=ub(i);
lb_i=lb(i);
Positions(:,i)=rand(N,1).*(ub_i-lb_i)+lb_i;
end
end
end

function [Positions]=corOppose2(Positions,ub,lb,upper,lower,dim,threshold,Row_Id)

[n,b] = size(Positions);
for i=1:n(1)

if i ~= Row_Id

sum=0;
greater=[];
less=[];
x=1;z=1;y=1;

for j=1:b
d(x)=abs(Positions(Row_Id,j)-Positions(i,j));
if d(x)<threshold
greater(y)=j;
y=y+1;
else
less(z)=j;
z=z+1;
end
sum=sum+d(x)*d(x);
x=x+1;
end
% sum
src=1-(double(6*sum))/(double(n(1)*(n(1)*n(1)-1)));
% src
if src<=0
if size(greater)<size(less)
% for j=1:size(less)
% dim=less(j);
% Positions(i,dim)=ub(dim)+lb(dim)-Positions(i,dim);
% end
else
for j=1:size(greater)
dim=greater(j);
Positions(i,dim)=(upper(1,dim)+lower(1,dim)-Positions(i,dim));
end
end
end
end
end
end




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