请用python写出灰狼优化算法的代码

以下是灰狼优化算法的Python代码：

import numpy as np

# 灰狼优化算法
def grey_wolf_optimizer(cost_function, lb, ub, dim, SearchAgents_no, Max_iter):

    # 初始化灰狼群
    Alpha_pos = np.zeros(dim)
    Beta_pos = np.zeros(dim)
    Delta_pos = np.zeros(dim)
    Alpha_score = float("inf")
    Beta_score = float("inf")
    Delta_score = float("inf")
    Positions = np.zeros((SearchAgents_no, dim))
    for i in range(dim):
        Positions[:, i] = np.random.uniform(0, 1, SearchAgents_no) * (ub[i] - lb[i]) + lb[i]

    # 开始迭代
    for l in range(0, Max_iter):

        for i in range(0, SearchAgents_no):

            # 计算适应度值
            fitness = cost_function(Positions[i, :])

            # 更新Alpha, Beta, Delta位置和适应度值
            if fitness < Alpha_score:
                Alpha_score = fitness
                Alpha_pos = Positions[i, :]
            if (fitness > Alpha_score) and (fitness < Beta_score):
                Beta_score = fitness
                Beta_pos = Positions[i, :]
            if (fitness > Alpha_score) and (fitness > Beta_score) and (fitness < Delta_score):
                Delta_score = fitness
                Delta_pos = Positions[i, :]

        # 更新狼群位置
        a = 2 - 2 * l / Max_iter
        for i in range(0, SearchAgents_no):
            r1 = np.random.rand(dim)
            r2 = np.random.rand(dim)
            A = 2 * a * r1 - a
            C = 2 * r2
            D_alpha = abs(C * Alpha_pos - Positions[i, :])
            X1 = Alpha_pos - A * D_alpha
            r1 = np.random.rand(dim)
            r2 = np.random.rand(dim)
            A = 2 * a * r1 - a
            C = 2 * r2
            D_beta = abs(C * Beta_pos - Positions[i, :])
            X2 = Beta_pos - A * D_beta
            r1 = np.random.rand(dim)
            r2 = np.random.rand(dim)
            A = 2 * a * r1 - a
            C = 2 * r2
            D_delta = abs(C * Delta_pos - Positions[i, :])
            X3 = Delta_pos - A * D_delta
            Positions[i, :] = (X1 + X2 + X3) / 3

    # 返回最优解和适应度值
    return Alpha_pos, Alpha_score

注意：此代码仅供参考，具体实现可能会因为不同的问题而有所不同。