灰狼算法路径规划python代码

以下是使用灰狼算法进行路径规划的 Python 代码示例：

import numpy as np

# 目标函数，输入为一个向量，输出为标量
def objective_function(x):
    return np.sum(x)

# 灰狼算法
def grey_wolf_optimizer(obj_func, lb, ub, dim, search_agent_num, max_iter):
    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")
    search_agents = np.zeros((search_agent_num, dim))
    for i in range(search_agent_num):
        search_agents[i, :] = np.random.uniform(lb, ub, dim)
    for iter_count in range(max_iter):
        for i in range(search_agent_num):
            fitness = obj_func(search_agents[i, :])
            if fitness < alpha_score:
                alpha_score = fitness
                alpha_pos = search_agents[i, :]
            if fitness > alpha_score and fitness < beta_score:
                beta_score = fitness
                beta_pos = search_agents[i, :]
            if fitness > alpha_score and fitness > beta_score and fitness < delta_score:
                delta_score = fitness
                delta_pos = search_agents[i, :]
        a = 2 - 2 * iter_count / max_iter
        for i 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 = abs(C1 * alpha_pos - search_agents[i, :])
            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 = abs(C2 * beta_pos - search_agents[i, :])
            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 = abs(C3 * delta_pos - search_agents[i, :])
            X3 = delta_pos - A3 * D_delta
            search_agents[i, :] = (X1 + X2 + X3) / 3
    return alpha_pos

# 示例：使用灰狼算法求解目标函数最小值
result = grey_wolf_optimizer(objective_function, -10, 10, 5, 30, 1000)
print(result)