写一段matlab中的灰狼算法

在MATLAB中，我们可以使用一种称为"Grey Wolf Optimizer" (GWO)的优化算法来进行求解。这是一个基于群智能的搜索算法，模拟了狼群的行为。以下是基本的GWO算法的伪代码：

function [x_best, f_min] = GWO(obj_func, lb, ub, num_iterations, num_wolves)
    % 初始化狼的位置、速度和等级
    wolves_pos = rand(num_wolves, num_variables, lb, ub); % 狼位置
    wolves_vel = zeros(num_wolves, num_variables);
    alpha = [wolves_pos; Inf*ones(1, num_variables)]; % 领头狼
    beta = [wolves_pos; Inf*ones(1, num_variables)]; % 次领头狼
    delta = [wolves_pos; Inf*ones(1, num_variables)]; % 第三领头狼
    
    for iter = 1:num_iterations
        % 更新狼的位置
        for i = 1:num_wolves
            A = 2 * rand(num_variables) - 1;
            C = 2 * abs(A .* randn(num_variables));
            X_alpha = AlphaPosition(iter, obj_func, lb, ub);
            X_beta = BetaPosition(iter, obj_func, lb, ub);
            X_delta = DeltaPosition(iter, obj_func, lb, ub);
            
            % Update velocities and positions
            WolvesVel(i,:) = C .* abs(X_alpha - WolvesPos(i,:)) + A .* abs(X_delta - WolvesPos(i,:));
            WolvesPos(i,:) = WolvesPos(i,:) + WolvesVel(i,:);
            
            % Update alpha, beta, and delta
            if isBetter(WolvesPos(i,:), obj_func, alpha)
                delta = beta;
                beta = alpha;
                alpha = WolvesPos(i,:);
            elseif isBetter(WolvesPos(i,:), obj_func, beta)
                delta = beta;
                beta = WolvesPos(i,:);
            elseif isBetter(WolvesPos(i,:), obj_func, delta)
                delta = WolvesPos(i,:);
            end
        end
    end
    x_best = alpha(1,:);
    f_min = objectiveValue(alpha(1,:));
end

% 辅助函数判断是否更新狼群等级
function val = isBetter(new_position, obj_func, old_position)
    new_val = obj_func(new_position);
    old_val = obj_func(old_position);
    if new_val < old_val
        return true;
    else
        return false;
    end
end

这个函数接受目标函数、变量范围、迭代次数和狼的数量作为输入，并返回全局最优解。请注意，这只是一个简化的版本，实际使用时需要根据具体需求进行调整。