Grey Wolf optimizer in matlab
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Hi, I am trying to write matlab code for grey wolf optimization. I would want to use it to obtain wbl patramaters. So I have written a trial code but if i run it (in R2016a) I keep getting error in line 95, telling me this"Error: File: Gwo1.m Line: 95 Column: 4
All functions in a script must be closed with an 'end'."When I insert the end at the speficidied location, it's telling me this "Error: File: Gwo1.m Line: 94 Column: 4
Function definitions are not permitted in this context.". How can I solve this? Below is the code that I used, Thanks in advance.
Any help will be highly appreciated. Thank you.
%X=input('enter the values of X\n')
X = rand(35,2);
out = wbl(X)
function out = wbl(X)
A1=X(:,1);
A2=X(:,2);
%objective function
fx=(2*sqrt(2).*A1+A2).*100;
g(:,1)=2.*(sqrt(2).*A1+A2)./(sqrt(2).*A1.^2+2.*A1.*A2)-2;
g(:,2)=2.*A2./(sqrt(2).*A1.^2+2.*A1.*A2)-2;
g(:,3)=2./(A1 + sqrt(2).*A2)-2;
%define penalty term
pp=10^9;
for i=1:size(g,1)
for j=1:size(g,2)
if g(i,j)>0
penalty(i,j)= pp.*g(i,j);
else
penalty(i,j)=0;
end
end
end
out = fx+sum(penalty,2);
%the GWO main code
format short
fun = @wbl;
N=300;
D=2;
lb=[0 0];
ub=[1 1];
itermax=100;
%generating intial popilation size
for i=1:N
for j=1:D
pos(i,j)=lb(j)+rand.*(ub(j)-lb(j));
end
end
%Evaluation of objective function
[fminvalue,ind]=min(fx);
% GWO main loop
iter=1;
while iter<=itermax
Fgbest=fminvalue;
a=2-2*iter/itermax;
for i=1:N
X=pos(i,:);
pos1=pos;
A1=2.*a.*rand(1,D)-a;
C1=2.*rand(1,D);
[alpha, alphaind]=min(fx);
alphapos=pos1(alphaind,:);
Dalpha=abs(C1.*alphapos-X);
X1=alphapos-A1.*Dalpha;
pos1(alphaind,:)=[];
fx1=fun(pos);
%funding beta position
[bet,betind]=min(fx1);
betpos=pos1(betind,:);
A2=2.*a.*rand(1,D)-a;
C2=2.*rand(1,D);
Dbet=abs(C2.*betpos-X);
X2=betpos-A2.*Dbet;
pos1(betind,:)=[];
fx1=fun(pos1);
%Delta position
[delta,deltaind]=min(fx1);
de;tapos=pos1(deltaind,:);
A3=2.*a.*rand(1,D)-a;
C3=2.*rand(1,D);
Ddelta=abs(C3.*betpos-X);
X3=deltapos-A3.*Ddelta;
Xnew=(X1+X2+X3)./3;
%check bound
Xnew=max(Xnew,lb);
Xnew=min(Xnew,ub);
fnew=fun(Xnew);
%greedy slection
if fnew<fx(i)
pos(i,:)=Xnew;
fx(i,:)=fnew;
end
end
%Update Gbest
[fmin,find]=min(fx);
if fmin<Fgbest
Fgbest = fmin;
gbest=pos(find,:);
end
%memorize
[optval,optind]=min(fx);
BestFx(iter)=optval;
BestX(iter,:)=pos(optind,:);
%show iteration infomation
plot(BestFx, 'LineWidth',2);
iter=iter+1
end
out = BestX
end
3 commentaires
okoth ochola
le 29 Jan 2023
Torsten
le 29 Jan 2023
As you can see, the code above runs without this problem.
So I can't give advice.
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