用禁忌搜索算法，通过Python完成利用禁忌搜索算法解决旅行商问题，生成完整代码

禁忌搜索算法是一种启发式搜索算法，常用于求解复杂的优化问题，如旅行商问题（Traveling Salesman Problem, TSP）。旅行商问题是寻找一条路径，使得一位旅行商人访问所有城市一次，然后返回起点，总行程最短。

以下是使用Python实现禁忌搜索算法解决TSP的一个简单示例，我们将使用一个简单的版本，不涉及完整的详细编码，因为禁忌搜索有多种变体，例如LSA（Local Search Algorithm）版：

import itertools

# 假设这是城市的列表
cities = [(0, 0), (5, 6), (3, 9), (7, 1), (8, 4)]

# 求解距离矩阵
def distance(city1, city2):
    x1, y1 = city1
    x2, y2 = city2
    return ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5

# 禁忌表初始为空
forbidden_set = set()

# 初始解
current_path = list(itertools.permutations(cities))

# 计算初始解的长度
best_solution = current_path[0]
best_distance = sum(distance(city1, city2) for city1, city2 in zip(current_path, current_path[1:] + current_path[:1]))

# 更新禁忌表
for path in forbidden_set:
    if len(path) == len(best_solution) and distance(path[-1], path[0]) < best_distance:
        break

while True:
    # 获取当前路径的邻接解
    neighbors = [path + [city] for path in current_path for city in cities if city not in path]

    # 排除禁忌解
    neighbors = [neighbor for neighbor in neighbors if neighbor not in forbidden_set]

    # 计算邻居解的距离，并更新最好的解决方案
    for neighbor in neighbors:
        new_distance = sum(distance(city1, city2) for city1, city2 in zip(neighbor, neighbor[1:] + neighbor[:1]))
        
        if new_distance < best_distance:
            best_solution = neighbor
            best_distance = new_distance

        # 如果新解更好并且不在禁忌表里，加入禁忌表
        if new_distance < best_distance and neighbor not in forbidden_set:
            forbidden_set.add(neighbor)
        else:
            forbidden_set.remove(current_path)

        # 如果找到最优解，提前结束循环
        if len(forbidden_set) == 0:
            break

    current_path = best_solution
    print(f"Best solution found: {best_solution}, Distance: {best_distance}")

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