补充上述代码中的布谷鸟算法和粒子群算法，其中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.粒子群算法的参数采用最常用的默认值。

下面是补充了布谷鸟算法和粒子群算法的代码：

import numpy as np
import matplotlib.pyplot as plt# 初始化传感器和锚节点的
num_sensors = 100
num_anchors = 30
sensor_positions = np.random.rand(num_sensors, 2) * 100
anchor_positions = np.random.rand(num_anchors, 2) * 100

# 布谷鸟算法优化传感器定位
def cuckoo_search(sensor_positions, anchor_positions, step_size_min=0.9, step_size_max=1.0, mutation_prob_min=0.05, mutation_prob_max=0.25, num_candidates=25, max_iterations=100):
    num_sensors = sensor_positions.shape[0]
    dim = sensor_positions.shape[1]
    best_solution = sensor_positions.copy()
    best_fitness = np.inf
    
    for iteration in range(max_iterations):
        # 生成候选解
        candidates = []
        for i in range(num_candidates):
            candidate = sensor_positions.copy()
            step_size = np.random.uniform(step_size_min, step_size_max)
            mutation_prob = np.random.uniform(mutation_prob_min, mutation_prob_max)
            candidate += step_size * np.random.randn(num_sensors, dim)
            candidate = np.clip(candidate, 0, 100)  # 限制在范围内
            mask = np.random.rand(num_sensors, dim) < mutation_prob
            candidate[mask] = np.random.rand(np.sum(mask)) * 100
            candidates.append(candidate)
        
        # 计算适应度函数值
        fitness_values = np.linalg.norm(candidates - anchor_positions[:, np.newaxis], axis=2).sum(axis=0)
        
        # 选择最好的解
        best_index = np.argmin(fitness_values)
        if fitness_values[best_index] < best_fitness:
            best_solution = candidates[best_index]
            best_fitness = fitness_values[best_index]
    
    return best_solution

# 粒子群算法优化传感器定位
def particle_swarm_optimization(sensor_positions, anchor_positions, num_particles=100, max_iterations=100, inertia_weight=0.5, cognitive_weight=0.5, social_weight=0.5):
    num_sensors = sensor_positions.shape[0]
    dim = sensor_positions.shape[1]
    positions = sensor_positions.copy()
    velocities = np.zeros((num_particles, num_sensors, dim))
    best_positions = positions.copy()
    best_fitness = np.inf
    global_best_position = positions[0].copy()
    
    for iteration in range(max_iterations):
        for i in range(num_particles):
            # 更新速度
            velocities[i] = (inertia_weight * velocities[i] +
                             cognitive_weight * np.random.rand() * (best_positions[i] - positions[i]) +
                             social_weight * np.random.rand() * (global_best_position - positions[i]))
            
            # 更新位置
            positions[i] += velocities[i]
            positions[i] = np.clip(positions[i], 0, 100)  # 限制在范围内
            
            # 计算适应度函数值
            fitness_value = np.linalg.norm(positions[i] - anchor_positions, axis=1).sum()
            
            # 更新个体最优解和全局最优解
            if fitness_value < np.linalg.norm(best_positions[i] - anchor_positions, axis=1).sum():
                best_positions[i] = positions[i].copy()
            if fitness_value < np.linalg.norm(global_best_position - anchor_positions, axis=1).sum():
                global_best_position = positions[i].copy()
        
    return global_best_position

# 优化传感器定位
optimized_sensor_positions = cuckoo_search(sensor_positions, anchor_positions)
optimized_sensor_positions = particle_swarm_optimization(optimized_sensor_positions, anchor_positions)

# 绘制定位前后的节点分布图
plt.scatter(sensor_positions[:, 0], sensor_positions[:, 1], label='Before Optimization')
plt.scatter(optimized_sensor_positions[:, 0], optimized_sensor_positions[:, 1], label='After Optimization')
plt.scatter(anchor_positions[:, 0], anchor_positions[:, 1], label='Anchors')
plt.legend()
plt.show()

# 计算定位误差值
error = np.linalg.norm(optimized_sensor_positions - sensor_positions, axis=1)
average_error = np.mean(error)
print('Average localization error:', average_error)

请注意，以上代码是一个简化的实现，参数的选择可能需要根据实际问题进行调整。同时，布谷鸟算法和粒子群算法的性能也受到参数设置的影响，可能需要通过实验进行调优。