Traceback (most recent call last): File "E:\Duzhuan\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3460, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-c833f4fdcae2>", line 1, in <module> runfile('C:\\Users\\zhangxiao\\Desktop\\Algorithm Integration\\apc_1.py', wdir='C:\\Users\\zhangxiao\\Desktop\\Algorithm Integration') File "E:\Duzhuan\JetBrainsPyCharm 2023.1\PyCharm 2023.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "E:\Duzhuan\JetBrainsPyCharm 2023.1\PyCharm 2023.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "C:\Users\zhangxiao\Desktop\Algorithm Integration\apc_1.py", line 189, in <module> integrated_optimization() File "C:\Users\zhangxiao\Desktop\Algorithm Integration\apc_1.py", line 177, in integrated_optimization x_after, y_after = final_solution[i] TypeError: cannot unpack non-iterable numpy.float64 object

这个错误是因为`final_solution`是一个`numpy.float64`类型的对象，无法进行解包操作。为了解决这个问题，您可以将`final_solution`的维度更改为`(num_sensors, 2)`，并在更新传感器位置时使用索引访问。下面是修改后的代码：

# 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][0], final_solution[i][1]
        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()

请尝试运行这段代码，看看是否解决了问题。如果还有其他问题，请随时提问。