用禁忌搜索算法解决TSP问题，用C++代码实现，且能通过读取文本文件中的数据作为输入，表明注释

好的，下面是用禁忌搜索算法解决 TSP 问题的 C++ 代码实现，能够通过读取文本文件中的数据作为输入。

注释在代码中标出。

#include <iostream>
#include <fstream>
#include <cstring>
#include <cstdlib>
#include <ctime>
#include <cmath>
#include <vector>
using namespace std;

#define MAX_ITER 5000        // 禁忌搜索最大迭代次数
#define MAX_TABU_SIZE 100    // 禁忌表最大长度
#define TABU_TENURE 10       // 禁忌期限
#define ALPHA 1              // 启发式因子
#define BETA 5               // 启发式因子

int n;                       // 城市数量
int max_distance;            // 最大距离
int tabu_size;               // 禁忌表长度
int tabu_list[MAX_TABU_SIZE]; // 禁忌表
int distance[100][100];      // 距离矩阵
int best_distance;           // 最优路径长度
int best_solution[100];      // 最优路径
vector<int> candidate_list;  // 候选集

// 读取数据文件
void read_data(const char* file_name) {
    ifstream fin(file_name);
    fin >> n;
    for (int i = 0; i < n; i++) {
        for (int j = 0; j < n; j++) {
            fin >> distance[i][j];
            max_distance = max(max_distance, distance[i][j]);
        }
    }
    fin.close();
}

// 计算路径长度
int calculate_distance(int* solution) {
    int len = 0;
    for (int i = 0; i < n - 1; i++) {
        len += distance[solution[i]][solution[i + 1]];
    }
    len += distance[solution[n - 1]][solution[0]];
    return len;
}

// 初始化候选集
void init_candidate_list() {
    for (int i = 1; i < n; i++) {
        candidate_list.push_back(i);
    }
}

// 获得启发式值
int get_heuristic(int cur_city, int next_city) {
    int h = 0;
    for (int i = 0; i < candidate_list.size(); i++) {
        int city = candidate_list[i];
        h += pow(max_distance - distance[cur_city][city], ALPHA) * pow(max_distance - distance[next_city][city], BETA);
    }
    return h;
}

// 选择下一个城市
int select_next_city(int cur_city, int* solution) {
    int next_city = -1;
    int best_h = -1;
    for (int i = 0; i < candidate_list.size(); i++) {
        int city = candidate_list[i];
        int h = get_heuristic(cur_city, city);
        if (h > best_h) {
            bool tabu = false;
            for (int j = 0; j < tabu_size; j++) {
                if (solution[tabu_list[j]] == city) {
                    tabu = true;
                    break;
                }
            }
            if (!tabu) {
                best_h = h;
                next_city = city;
            }
        }
    }
    if (next_city == -1) {
        next_city = candidate_list[rand() % candidate_list.size()];
    }
    return next_city;
}

// 更新禁忌表
void update_tabu_list(int city) {
    for (int i = tabu_size - 1; i >= 1; i--) {
        tabu_list[i] = tabu_list[i - 1];
    }
    tabu_list[0] = city;
}

// 禁忌搜索
void tabu_search() {
    int iter = 0;
    int solution[n];
    for (int i = 0; i < n; i++) {
        solution[i] = i;
    }
    solution[0] = rand() % n;
    best_distance = calculate_distance(solution);
    memcpy(best_solution, solution, sizeof(best_solution));
    while (iter < MAX_ITER) {
        candidate_list.clear();
        for (int i = 0; i < n; i++) {
            if (i != solution[iter % n] && i != solution[(iter + 1) % n]) {
                candidate_list.push_back(i);
            }
        }
        int next_city = select_next_city(solution[iter % n], solution);
        memcpy(solution, best_solution, sizeof(best_solution));
        swap(solution[(iter + 1) % n], solution[next_city]);
        int cur_distance = calculate_distance(solution);
        if (cur_distance < best_distance) {
            best_distance = cur_distance;
            memcpy(best_solution, solution, sizeof(best_solution));
            update_tabu_list(next_city);
        }
        iter++;
    }
}

int main() {
    srand(time(NULL));
    read_data("data.txt");
    tabu_size = min(n / 2, MAX_TABU_SIZE);
    init_candidate_list();
    tabu_search();
    cout << "Best distance: " << best_distance << endl;
    cout << "Best solution: ";
    for (int i = 0; i < n; i++) {
        cout << best_solution[i] << " ";
    }
    cout << endl;
    return 0;
}

该程序使用禁忌搜索算法解决 TSP 问题，读取数据文件获取距离矩阵。程序中使用了最大距离、禁忌表长度、禁忌期限、启发式因子等参数。使用禁忌搜索算法解决问题的主要思路是，在候选集中选择下一个城市，选择时考虑禁忌表和启发式值。最后输出最优路径长度和最优路径。