灰狼算法Python代码

以下是基于Python的灰狼算法代码示例：

import numpy as np

def obj_func(x):
    # 目标函数，这里以f(x) = x^2为例
    return np.sum(np.power(x, 2))

def grey_wolf_optimizer(obj_func, lb, ub, dim, search_agent_num, max_iter):
    # obj_func: 目标函数
    # lb: 取值下界
    # ub: 取值上界
    # dim: 变量个数
    # search_agent_num: 狼群数量
    # max_iter: 最大迭代次数

    # 初始化搜索代理
    positions = np.random.uniform(lb, ub, (search_agent_num, dim))

    alpha_pos = np.zeros(dim)
    alpha_score = float("inf")

    beta_pos = np.zeros(dim)
    beta_score = float("inf")

    delta_pos = np.zeros(dim)
    delta_score = float("inf")

    for i in range(max_iter):
        # 计算每个搜索代理到三个灰狼的距离
        for j in range(search_agent_num):
            fitness = obj_func(positions[j])
            if fitness < alpha_score:
                alpha_score = fitness
                alpha_pos = positions[j]
            if fitness > alpha_score and fitness < beta_score:
                beta_score = fitness
                beta_pos = positions[j]
            if fitness > alpha_score and fitness > beta_score and fitness < delta_score:
                delta_score = fitness
                delta_pos = positions[j]

        a = 2 - 2 * i / max_iter  # 确定a值
        for j in range(search_agent_num):
            r1 = np.random.rand(dim)
            r2 = np.random.rand(dim)
            A1 = 2 * a * r1 - a
            C1 = 2 * r2
            D_alpha = np.abs(C1 * alpha_pos - positions[j])
            X1 = alpha_pos - A1 * D_alpha

            r1 = np.random.rand(dim)
            r2 = np.random.rand(dim)
            A2 = 2 * a * r1 - a
            C2 = 2 * r2
            D_beta = np.abs(C2 * beta_pos - positions[j])
            X2 = beta_pos - A2 * D_beta

            r1 = np.random.rand(dim)
            r2 = np.random.rand(dim)
            A3 = 2 * a * r1 - a
            C3 = 2 * r2
            D_delta = np.abs(C3 * delta_pos - positions[j])
            X3 = delta_pos - A3 * D_delta

            positions[j] = (X1 + X2 + X3) / 3

    return alpha_pos, alpha_score

其中，`obj_func`是目标函数，`lb`和`ub`是取值下界和上界，`dim`是变量个数，`search_agent_num`是狼群数量，`max_iter`是最大迭代次数。函数返回最优解和最优解对应的函数值。