GWOＥＰＤ算法的matlab代码

以下是GWO-EPD算法的Matlab代码：

function [bestSol, bestFit] = GWO_EPD(fobj, dim, lb, ub, maxIter, N, C, eps)
% GWO_EPD: Grey wolf optimizer with enhanced prey death mechanism
%
% INPUTS
% fobj: objective function
% dim: dimensionality of the problem
% lb: lower bound of the search space
% ub: upper bound of the search space
% maxIter: maximum number of iterations
% N: number of grey wolves
% C: constant parameter of the prey death mechanism
% eps: small constant to avoid division by zero
%
% OUTPUTS
% bestSol: best solution found
% bestFit: best fitness value found
%
% Reference:
% Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014).
% Grey Wolf Optimizer: A New Optimization Algorithm Inspired by Wolf Pack Behavior.
% Advances in Engineering Software, 69, 46-61.

% Initialize the grey wolves
alpha.pos = lb + rand(1, dim) .* (ub - lb);
alpha.fit = fobj(alpha.pos);

beta.pos = lb + rand(1, dim) .* (ub - lb);
beta.fit = fobj(beta.pos);

delta.pos = lb + rand(1, dim) .* (ub - lb);
delta.fit = fobj(delta.pos);

for i = 1:N
    wolf(i).pos = lb + rand(1, dim) .* (ub - lb);
    wolf(i).fit = fobj(wolf(i).pos);
end

% Main loop
for t = 1:maxIter
    % Update alpha, beta, and delta
    for i = 1:N
        if wolf(i).fit < alpha.fit
            delta = beta;
            beta = alpha;
            alpha = wolf(i);
        elseif wolf(i).fit < beta.fit
            delta = beta;
            beta = wolf(i);
        elseif wolf(i).fit < delta.fit
            delta = wolf(i);
        end
    end
    
    % Update the position of each wolf
    for i = 1:N
        A = 2 * C * rand(1, dim) - C; % Equation (15)
        D_alpha = abs(C * alpha.pos - wolf(i).pos);
        X1 = alpha.pos - A .* D_alpha; % Equation (17)
        
        A = 2 * C * rand(1, dim) - C; % Equation (15)
        D_beta = abs(C * beta.pos - wolf(i).pos);
        X2 = beta.pos - A .* D_beta; % Equation (18)
        
        A = 2 * C * rand(1, dim) - C; % Equation (15)
        D_delta = abs(C * delta.pos - wolf(i).pos);
        X3 = delta.pos - A .* D_delta; % Equation (19)
        
        wolf(i).pos = (X1 + X2 + X3) / 3; % Equation (16)
        
        % Apply boundary constraints
        wolf(i).pos(wolf(i).pos < lb) = lb(wolf(i).pos < lb);
        wolf(i).pos(wolf(i).pos > ub) = ub(wolf(i).pos > ub);
        
        % Evaluate the fitness of the new position
        wolf(i).fit = fobj(wolf(i).pos);
    end
    
    % Apply the enhanced prey death mechanism
    for i = 1:N
        if wolf(i).fit > alpha.fit + eps % Equation (20)
            wolf(i).pos = alpha.pos + rand(1, dim) .* (beta.pos - delta.pos);
            wolf(i).fit = fobj(wolf(i).pos);
        end
    end
    
    % Update the best solution found so far
    [bestFit, bestIdx] = min([wolf.fit]);
    bestSol = wolf(bestIdx);
    
    % Display the iteration and the best fitness value found so far
    fprintf('Iteration %d: Best fitness value = %.4f\n', t, bestFit);
end
end

其中，fobj是目标函数，dim是问题的维度，lb和ub是搜索空间的下限和上限，maxIter是最大迭代次数，N是狼群中狼的数量，C是常数参数，eps是避免除以零的小常数。函数的输出是最佳解和最佳适应度值。