能否提供一个使用禁忌搜索算法（Tabu Search）来求解旅行商问题（Traveling Salesman Problem, TSP）的Python代码示例？

禁忌搜索算法（Tabu Search, TS）是一种局部搜索优化算法，常用于求解复杂优化问题，包括旅行商问题(TSP)。在Python中，我们可以使用`tsplib`库结合自定义的Tabu List策略来实现。这里给出一个简化的例子，不包含完整的细节，但可以帮助你理解其基本原理：

首先，你需要安装`tsplib`库（如果还没有安装）：

pip install tsplib95

然后，下面是一个简单的Tabu Search求解TSP问题的Python代码示例：

import tsplib95
import itertools
import numpy as np

def tabu_search(tsp_instance, initial_solution, tabu_list_size=5, max_iterations=100):
    tsp_problem = tsplib95.load(tsp_instance)
    n_cities = tsp_problem.n
    best_solution = initial_solution
    best_cost = tsp_problem.objective(best_solution)

    # 初始化Tabu List
    tabu_list = []

    for _ in range(max_iterations):
        current_cost = tsp_problem.objective(current_solution)
        if current_cost < best_cost:
            best_cost = current_cost
            best_solution = current_solution.copy()

        # 扩展邻域
        neighbors = [solution for solution in generate_neighbors(best_solution, tsp_problem)]

        for neighbor in neighbors:
            # 避免陷入局部最优
            if is_tabu(neighbor, tabu_list, tabu_list_size):
                continue

            candidate_cost = tsp_problem.objective(neighbor)
            if candidate_cost < current_cost:
                current_cost = candidate_cost
                current_solution = neighbor
                tabu_list.append(current_solution)
                if len(tabu_list) > tabu_list_size:
                    tabu_list.pop(0)

    return best_solution, best_cost

# 生成邻居函数
def generate_neighbors(solution, tsp_problem):
    for i in range(len(solution)):
        for j in range(i + 1, len(solution)):
            yield solution[:i] + solution[j:] + [solution[i], solution[j]]

# 判断邻居是否在Tabu列表中
def is_tabu(neighbor, tabu_list, tabu_list_size):
    for _ in range(tabu_list_size):
        if neighbor == tabu_list[-1]:
            return True
        tabu_list.pop(0)
    return False

# 示例用法
tsp_instance = 'your_tsp_instance.tsp'  # 用实际的TSP实例文件替换
initial_solution = np.random.permutation(range(tsp_problem.n))  # 随机初始解

best_solution, best_cost = tabu_search(tsp_instance, initial_solution)
print(f"Best solution: {best_solution}")
print(f"Best cost: {best_cost}")