根据布谷鸟搜索核心原理，创作出新的布谷鸟搜索代码用python写

布谷鸟搜索是一种基于种群的启发式算法，其核心思想是利用不同的搜索策略来避免陷入局部最优解。下面是一个用Python实现的布谷鸟搜索代码示例：

import numpy as np

# 目标函数
def objective_function(x):
    return np.sum(x ** 2)

# 初始化布谷鸟种群
def initialize_cuckoos(num_cuckoos, num_dimensions):
    return np.random.uniform(low=-5.0, high=5.0, size=(num_cuckoos, num_dimensions))

# 获取当前最优解
def get_best_solution(cuckoos, objective_function):
    fitness = np.zeros(len(cuckoos))
    for i in range(len(cuckoos)):
        fitness[i] = objective_function(cuckoos[i])
    best_index = np.argmin(fitness)
    return cuckoos[best_index], fitness[best_index]

# 生成新解
def generate_new_solution(cuckoo, best_solution, step_size):
    u = np.random.normal(size=cuckoo.shape)
    v = np.random.normal(size=cuckoo.shape)
    cuckoo_new = cuckoo + step_size * u + step_size * (best_solution - cuckoo) * v
    return cuckoo_new

# 布谷鸟搜索算法
def cuckoo_search(num_cuckoos, num_dimensions, max_iterations, objective_function):
    cuckoos = initialize_cuckoos(num_cuckoos, num_dimensions)
    best_solution, best_fitness = get_best_solution(cuckoos, objective_function)
    for i in range(max_iterations):
        # 生成新解
        new_cuckoos = np.zeros(cuckoos.shape)
        for j in range(num_cuckoos):
            new_cuckoos[j] = generate_new_solution(cuckoos[j], best_solution, 0.1)
        # 更新最优解
        new_solution, new_fitness = get_best_solution(new_cuckoos, objective_function)
        if new_fitness < best_fitness:
            best_solution, best_fitness = new_solution, new_fitness
        # 替换部分巢穴
        indices = np.random.randint(num_cuckoos, size=int(0.2 * num_cuckoos))
        cuckoos[indices] = new_cuckoos[indices]
    return best_solution, best_fitness

使用示例：

best_solution, best_fitness = cuckoo_search(num_cuckoos=20, num_dimensions=10, max_iterations=1000, objective_function=objective_function)
print('Best solution:', best_solution)
print('Best fitness:', best_fitness)

在这个例子中，我们使用布谷鸟搜索算法来最小化一个简单的函数。你可以根据自己的需求来修改算法参数和目标函数。