布谷鸟算法调参python

布谷鸟算法是一种优化算法，用于搜索最优解。在Python中，可以使用布谷鸟算法进行参数调优。下面是一个简单的布谷鸟算法的Python实现示例：

import random
import numpy as np

def cuckoo_search(objective_func, bounds, num_nests, num_iterations):
    # 初始化鸟巢的位置
    nests = np.random.uniform(bounds\[0\], bounds\[1\], (num_nests, len(bounds)))
    best_nest = None
    best_fitness = float('inf')

    for i in range(num_iterations):
        # 对每个鸟巢进行搜索
        for j in range(num_nests):
            # 生成新的解
            new_nest = nests\[j\] + np.random.randn(*bounds.shape) * 0.01
            new_nest = np.clip(new_nest, bounds\[0\], bounds\[1\])

            # 计算适应度值
            fitness = objective_func(new_nest)

            # 更新最优解
            if fitness < best_fitness:
                best_nest = new_nest
                best_fitness = fitness

            # 选择是否替换原有鸟巢
            if fitness < objective_func(nests\[j\]):
                nests\[j\] = new_nest

        # 随机选择一些鸟巢进行迁移
        num_migrants = int(num_nests * 0.2)
        migrants = np.random.randint(0, num_nests, num_migrants)
        for migrant in migrants:
            # 选择一个随机的鸟巢进行迁移
            dest = np.random.randint(0, num_nests)
            nests\[dest\] = nests\[migrant\]

    return best_nest, best_fitness

# 定义目标函数
def objective_func(x):
    return (x - 0.5) ** 2 + 1

# 设置参数范围
bounds = np.array(\[\[0, 1\]\])

# 调用布谷鸟算法进行参数调优
best_solution, best_fitness = cuckoo_search(objective_func, bounds, num_nests=10, num_iterations=100)

print("最优解:", best_solution)
print("最优适应度值:", best_fitness)

在这个示例中，我们定义了一个目标函数`objective_func`，并设置了参数范围`bounds`。然后，我们调用`cuckoo_search`函数进行参数调优，指定了鸟巢数量`num_nests`和迭代次数`num_iterations`。最后，打印出最优解和最优适应度值。

请注意，这只是一个简单的示例，实际使用时可能需要根据具体问题进行适当的调整和优化。
#### 引用[.reference_title]
- *1* *2* [布谷鸟算法详细讲解](https://blog.csdn.net/u013631121/article/details/76944879)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^koosearch_v1,239^v3^insert_chatgpt"}} ] [.reference_item]
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