code gwo in matlab

main.m
% Define the objective function to be optimized
objectiveFunction = @(x) x(1)^2 + x(2)^2;

% Set the number of wolves and dimensions, and the search space boundaries
nWolves = 15;
nDimensions = 2;
lowerBound = -10;
upperBound = 10;

% Set the maximum number of iterations
maxIterations = 100;

% Initialize the population randomly within the search space
population = rand(nWolves, nDimensions) * (upperBound - lowerBound) + lowerBound;

% Perform the main loop of the algorithm
for iteration = 1:maxIterations
    % Evaluate the fitness of each wolf
    fitness = objectiveFunction(population);

    % Update the positions of the alpha, beta, and delta wolves
    [bestFitness, bestIndex] = min(fitness);
    alphaWolf = population(bestIndex, :);
    [secondBestFitness, secondBestIndex] = min(fitness(fitness ~= bestFitness));
    betaWolf = population(secondBestIndex, :);
    deltaWolf = population(randi(nWolves), :);

    % Update the positions of the other wolves
    for i = 1:nWolves
        if i ~= bestIndex && i ~= secondBestIndex
            % Select two random wolves
            wolf1Index = randi(nWolves);
            wolf2Index = randi(nWolves);

            % Update the position of the current wolf
            population(i, :) = (population(wolf1Index, :) + population(wolf2Index, :)) / 2;
        end
    end

    % Apply the levy flight operator
    levyFlight = @(x) x + 0.01 .* randn(1);
    population = arrayfun(levyFlight, population);

    % Apply the boundary constraints
    population(population < lowerBound) = lowerBound;
    population(population > upperBound) = upperBound;
end

% Return the best solution found
[bestFitness, bestIndex] = min(fitness);
bestSolution = population(bestIndex, :);
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