Grey Wolf optimizer in matlab

Question

okoth ochola 2023-1-29

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1902660-grey-wolf-optimizer-in-matlab

回答： Steven Lord 2023-1-29

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)
Out of memory. The likely cause is an infinite recursion within the program.

Error in solution>wbl (line 55)
        fx1=fun(pos);
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