哈里斯鹰HHO优化算法Python代码

哈里斯鹰优化算法（Harris Hawks Optimization, HHO）是一种基于鹰群行为的优化算法，用于解决优化问题。下面是一个简单的HHO算法的Python代码示例：

import random
import numpy as np

def hho_algorithm(objective_function, lb, ub, dim, max_iter, num_hawks):
    # 初始化鹰的位置和速度
    hawks_position = np.zeros((num_hawks, dim))
    hawks_velocity = np.zeros((num_hawks, dim))
    hawks_fitness = np.zeros(num_hawks)

    # 初始化最优解和最优适应度
    best_solution = np.zeros(dim)
    best_fitness = float("inf")

    # 随机初始化鹰的位置
    for i in range(num_hawks):
        hawks_position[i] = lb + (ub - lb) * np.random.rand(dim)
        hawks_fitness[i] = objective_function(hawks_position[i])

        # 更新最优解和最优适应度
        if hawks_fitness[i] < best_fitness:
            best_solution = hawks_position[i]
            best_fitness = hawks_fitness[i]

    # 主循环
    for t in range(max_iter):
        # 更新每只鹰的速度和位置
        for i in range(num_hawks):
            # 随机选择两只鹰作为“追逐者”和“被追逐者”
            r1 = random.randint(0, num_hawks - 1)
            r2 = random.randint(0, num_hawks - 1)
            while r1 == i or r2 == i or r1 == r2:
                r1 = random.randint(0, num_hawks - 1)
                r2 = random.randint(0, num_hawks - 1)

            # 更新速度和位置
            hawks_velocity[i] = hawks_velocity[i] + (hawks_position[r1] - hawks_position[i]) + (hawks_position[r2] - hawks_position[i])
            hawks_position[i] = hawks_position[i] + hawks_velocity[i]

            # 边界处理
            hawks_position[i] = np.clip(hawks_position[i], lb, ub)

            # 更新适应度
            hawks_fitness[i] = objective_function(hawks_position[i])

            # 更新最优解和最优适应度
            if hawks_fitness[i] < best_fitness:
                best_solution = hawks_position[i]
                best_fitness = hawks_fitness[i]

    return best_solution, best_fitness

在上述代码中，`objective_function`是待优化的目标函数，`lb`和`ub`是变量的上下界，`dim`是变量的维度，`max_iter`是最大迭代次数，`num_hawks`是鹰的数量。算法通过不断更新鹰的速度和位置来寻找最优解。

请注意，这只是一个简单的示例代码，实际使用时可能需要根据具体问题进行适当的修改和调整。