尝试用Python写出一个可运行的具体实例

下面是一个简单的示例，展示了如何使用Python实现布谷鸟算法、遗传算法和粒子群优化算法的集成框架。

import random
import numpy as np

# 布谷鸟算法
def cuckoo_search(obj_func, num_nests, num_iterations):
    # 初始化种群
    nests = np.random.uniform(low=0, high=1, size=(num_nests, 2))
    best_nest = None

    for _ in range(num_iterations):
        # 更新最好的鸟巢
        if best_nest is None:
            best_nest = nests[0]
        else:
            for nest in nests:
                if obj_func(nest) < obj_func(best_nest):
                    best_nest = nest

        # 随机选择一个蛋巢进行搜索
        random_nest = random.choice(nests)
        new_nest = random_nest + np.random.standard_normal(size=2) * 0.01

        # 如果新的蛋巢更优，则替换原来的蛋巢
        if obj_func(new_nest) < obj_func(random_nest):
            random_nest = new_nest

    return best_nest

# 遗传算法
def genetic_algorithm(obj_func, population_size, num_generations):
    # 初始化种群
    population = np.random.uniform(low=0, high=1, size=(population_size, 2))
    best_individual = None

    for _ in range(num_generations):
        # 计算适应度
        fitness_scores = [obj_func(individual) for individual in population]

        # 选择父代
        parents = random.choices(population, weights=fitness_scores, k=population_size)

        # 交叉和变异
        offspring = []
        for _ in range(population_size):
            parent1, parent2 = random.sample(parents, 2)
            child = (parent1 + parent2) / 2
            child += np.random.standard_normal(size=2) * 0.01
            offspring.append(child)

        # 更新最佳个体
        if best_individual is None:
            best_individual = population[0]
        else:
            for individual in population:
                if obj_func(individual) < obj_func(best_individual):
                    best_individual = individual

        population = offspring

    return best_individual

# 粒子群优化算法
def particle_swarm_optimization(obj_func, num_particles, num_iterations):
    # 初始化粒子位置和速度
    positions = np.random.uniform(low=0, high=1, size=(num_particles, 2))
    velocities = np.random.uniform(low=0, high=1, size=(num_particles, 2))

    # 初始化全局最优位置和个体最优位置
    global_best_position = positions[0]
    individual_best_positions = positions.copy()

    for _ in range(num_iterations):
        # 更新粒子速度和位置
        inertia_weight = 0.8
        cognitive_weight = 1.5
        social_weight = 1.5

        for i in range(num_particles):
            velocities[i] = (inertia_weight * velocities[i] +
                             cognitive_weight * np.random.uniform(0, 1) * (individual_best_positions[i] - positions[i]) +
                             social_weight * np.random.uniform(0, 1) * (global_best_position - positions[i]))

            positions[i] += velocities[i]

        # 更新个体最优位置和全局最优位置
        for i in range(num_particles):
            if obj_func(positions[i]) < obj_func(individual_best_positions[i]):
                individual_best_positions[i] = positions[i]
                if obj_func(positions[i]) < obj_func(global_best_position):
                    global_best_position = positions[i]

    return global_best_position

# 示例目标函数，这里使用一个简单的二维函数作为示例
def objective_function(x):
    return np.sum(np.square(x))

# 使用集成框架进行优化
def integrated_optimization():
    num_nests = 10
    num_generations = 10
    num_particles = 10

    # 使用布谷鸟算法进行全局搜索
    global_best_nest = cuckoo_search(objective_function, num_nests, num_generations)

    # 使用遗传算法进行局部搜索
    local_best_individual = genetic_algorithm(objective_function, num_nests, num_generations)

    # 使用粒子群优化算法进行微调
    final_solution = particle_swarm_optimization(objective_function, num_particles, num_generations)

    print("Global best nest:", global_best_nest)
    print("Local best individual:", local_best_individual)
    print("Final solution:", final_solution)

# 运行集成优化框架
integrated_optimization()

请注意，这只是一个简单的示例，实际应用中可能需要根据具体问题进行调整和改进。