Step-by-Step Working of Grey Wolf Optimizer (GWO) with Numerical Example

This video explains how the GWO algorithm works with a numerical example.

MATLAB Codes
Their Working

[saf]

I appreciate the information and the amazing explanation you shared. You've hit the mark perfectly. Many thanks

[saejfn]

I have read the paper, and other papers that cited the main one. I tried to imagine how the algorithm worked, but until I see your numerical example evrything made sense. Thank you

[HakanÖztürk-zs]

Thanks. How can we find the weaknesses and strengths of such algorithms?

[satyanarayanagogula]

What an excellent explanation sir... Very useful for learner like me.. thanks a lot sir

[vivianecristhynebiniconte]

The best explanation. Thank you so much.

[Ritzieworld]

Sir please explain multiobjective GWO and multiobjective PSo .. with your explanation every doubt gets clear.. thankyou sir .. not able to find any example for the same

[ankitverma]

Thanks a lot, Dr. harish sir. Can you please explain Ant Lion Optimization algorithm in the same way? i.e Mathematically and in MATLAB CODE. Thanks in advance.

[pankajymca]

Good explanation sir, can you please make any video in which we can use this GWO in any controllers.

[saranchanpuang]

I will appreciate if you provide the mathlab code for solving this example.

[meghavar]

sir if possible, make a video on Aquila optimizer.

[namhunkim]

Thanks for the Great Video! It helps me a lot. However, I think there is some mistake of slides about 2nd iteration. Best, 2nd, 3rd position are not changed.

[zollen]

Is GWO good for finding maxima or minima only?

[srinivasarukonda]

Harish sir kindly make a video on Rao algorithm for feature selection and svm parameter tuning

[mustafasalah]

I have the question why the xnew is divided on 3 why this 3 in 9:37

[kausaraltaf]

Aoa Sir,
I have to optimize a function using gwo.
f= (exp(x1)/1+exp ( y1+w1))+

But I don't how to code this function in gwo
It's lb=0 and ub =1 with no constraints
Sir need help .

[JyotiYadav-yucx]

Please explain multi objective gwo with example

[ADA_asukadigital_academy]

Hello Dr.Harish Garg, I get convergence problem. How to solve it?
function out= assen(x)
X1= x(:, 1);
X2= x(:, 2);


%% Compute objective function
out=fx;
end

format short % display 4 digits
clear all
clc
%% Initialize the Parameters
N=5;
D=2;
lb=[-5 -5];
ub=[ 5 5];
itermax= 200;
%% Generate Intial Population
for i=1:N
for j=1:D
pos(i, j)=lb(j)+rand.*ub(j)-lb(j);
end
end
%% Evaluation of Objective function
fx=fun(pos);
%% Initialazation Gbest
[fminvalue, ind]=min(fx);
gbest=pos(ind, :);
%% GWO main loop start
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(pos1);
%% finding beta psition
[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);
%% finding Delta position
[delta, deltaind]=min(fx1);
deltapos=pos1(deltaind, :);
A3=2.*a.*rand(1, D)-a;
C3=2.*rand(1, D);
Ddelta=abs(C3.*deltapos-X);
X3=deltapos-A3.*Ddelta;
Xnew=(X1+X2+X3)./3;
%% Check the bound
Xnew=max(Xnew, lb);
Xnew=min(Xnew, lb);
fnew=fun(Xnew);
%% Greedy selection
if fnew<fx(i)
pos(i, :)=Xnew;
fx(i, :)=fnew
end
end % end of for loop
%% Update Gbest
[fmin, find]=min(fx);
if fmin<Fgbest
Fgbest=fmin;
gbest=pos(find, :);
end
%% Memorize the best
[optval, optind]=min(fx);
BestFx(iter)=optval;
BestX(iter, :)=pos(optind, :);
disp(['Iteration' num2str(iter) ': Best Cost =' num2str(BestFx(iter))])

iter=iter+1;
end
plot(BestFx, 'Linewidth', 2);
xlabel('Iteration number');
ylabel('Fitness Value');
title('Convegence Vs Iteration')
grid on

[assss]

Dr if i want use only x1 in initial population and then calculat fitness what i do?

[AncySherinJose]

Thankyou sir for the great explanation... I couldnt understand the working until I saw this video...Thankyou so much sir.

[thiagoamericano]

Very good explanation! Everything looks much easier after an numerical example.