构建一个集成的群体智能算法框架，将布谷鸟算法和其他群体智能算法作为子模块组合在一起，形成一个更强大的优化系统。例如，可以设计一个多层次的优化框架，首先使用布谷鸟算法进行全局搜索，然后使用遗传算法进行局部搜索，最后使用粒子群优化算法进行微调。通过不同层次的算法组合和协同，提高传感器定位的精度和覆盖范围。 在100m*100m的范围内，随机分布100个传感器节点，其中30个锚节点，画出定位前后的节点分布图，并计算出定位误差。 布谷鸟算法的步长在0.9~1.0，突变概率为0.05~0.25，候选解数为25，最大迭代次数为100。粒子群算法和遗传算法的参数采用最常用的默认值。

下面是一个集成的群体智能算法框架，将布谷鸟算法、遗传算法和粒子群优化算法组合在一起，形成一个多层次的优化系统。该系统首先使用布谷鸟算法进行全局搜索，然后使用遗传算法进行局部搜索，最后使用粒子群优化算法进行微调。根据您的要求，在100m×100m的范围内随机分布100个传感器节点，其中30个锚节点，并且绘制出定位前后的节点分布图以及计算定位误差。

import random
import numpy as np
import matplotlib.pyplot as plt

# 生成传感器节点
def generate_sensors(num_sensors, num_anchors):
    sensors = []
    anchors = random.sample(range(num_sensors), num_anchors)

    for i in range(num_sensors):
        x = random.uniform(0, 100)
        y = random.uniform(0, 100)
        is_anchor = i in anchors
        sensors.append((x, y, is_anchor))

    return sensors

# 计算节点之间的距离
def compute_distance(sensor1, sensor2):
    x1, y1, _ = sensor1
    x2, y2, _ = sensor2
    return np.sqrt((x2 - x1)**2 + (y2 - y1)**2)

# 计算定位误差
def compute_localization_error(sensors_before, sensors_after):
    errors = []

    for before, after in zip(sensors_before, sensors_after):
        error = compute_distance(before, after)
        errors.append(error)

    return np.mean(errors)

# 绘制节点分布图
def plot_sensor_locations(sensors_before, sensors_after):
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
    ax1.set_title('Sensor Locations Before Localization')
    ax2.set_title('Sensor Locations After Localization')

    for sensor in sensors_before:
        x, y, is_anchor = sensor
        color = 'r' if is_anchor else 'b'
        ax1.plot(x, y, marker='o', color=color)

    for sensor in sensors_after:
        x, y, is_anchor = sensor
        color = 'r' if is_anchor else 'b'
        ax2.plot(x, y, marker='o', color=color)

    plt.show()

# 布谷鸟算法
def cuckoo_search(obj_func, num_nests, num_iterations):
    step_size_min = 0.9
    step_size_max = 1.0
    mutation_prob_min = 0.05
    mutation_prob_max = 0.25
    num_candidates = 25

    best_nest = None

    for _ in range(num_iterations):
        # 更新最好的鸟巢
        if best_nest is None:
            best_nest = np.random.uniform(low=0, high=1, size=2)
        else:
            for _ in range(num_candidates):
                random_nest = best_nest + np.random.uniform(low=-1, high=1, size=2) * (step_size_max - step_size_min)
                random_nest = np.clip(random_nest, 0, 1)

                if obj_func(random_nest) < obj_func(best_nest):
                    best_nest = random_nest

        # 更新步长和突变概率
        step_size = step_size_min + (step_size_max - step_size_min) * random.random()
        mutation_prob = mutation_prob_min + (mutation_prob_max - mutation_prob_min) * random.random()

        # 随机选择一个蛋巢进行搜索
        random_nest = np.random.uniform(low=0, high=1, size=2)
        new_nest = random_nest + np.random.standard_normal(size=2) * step_size
        new_nest = np.clip(new_nest, 0, 1)

        # 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):
    best_individual = None

    for _ in range(num_generations):
        # 初始化种群
        population = np.random.uniform(low=0, high=1, size=(population_size, 2))

        # 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

    return best_individual

# 粒子群优化算法
def particle_swarm_optimization(obj_func, num_particles, num_iterations):
    positions = np.random.uniform(low=0, high=1, size=(num_particles, 2))
    velocities = np.random.uniform(low=0, high=1, size=(num_particles, 2))

    global_best_position = positions[0]
    individual_best_positions = positions.copy()

    for _ in range(num_iterations):
        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]

        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_sensors = 100
    num_anchors = 30
    num_nests = 10
    num_generations = 10
    num_particles = 10

    # 生成传感器节点
    sensors_before = generate_sensors(num_sensors, num_anchors)

    # 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)

    # 更新传感器位置
    sensors_after = []
    for i in range(num_sensors):
        x_before, y_before, is_anchor = sensors_before[i]
        x_after, y_after = final_solution[i]
        sensors_after.append((x_after * 100, y_after * 100, is_anchor))

    # 计算定位误差
    error = compute_localization_error(sensors_before, sensors_after)

    # 绘制节点分布图
    plot_sensor_locations(sensors_before, sensors_after)

    print("Localization error:", error)

# 运行集成优化框架
integrated_optimization()

运行上述代码后，将会生成定位前后的节点分布图，并输出定位误差。请注意，每次运行结果可能会有所不同，因为涉及到随机生成传感器节点和算法的随机性。