使用ALNS算法求解TSP问题的MATLAB的代码

以下是一个使用ALNS算法求解TSP问题的MATLAB代码示例：

% TSP问题的ALNS算法求解

% 定义TSP问题的距离矩阵
distMatrix = [
    0 29 82 46 68 52 72 42 51 55 29 74;
    29 0 55 46 42 43 43 23 23 31 41 51;
    82 55 0 68 46 55 23 43 41 29 79 42;
    46 46 68 0 82 15 72 31 62 42 21 51;
    68 42 46 82 0 74 23 52 21 46 82 58;
    52 43 55 15 74 0 61 23 55 31 33 37;
    72 43 23 72 23 61 0 42 23 31 77 37;
    42 23 43 31 52 23 42 0 33 15 37 33;
    51 23 41 62 21 55 23 33 0 29 62 46;
    55 31 29 42 46 31 31 15 29 0 51 21;
    29 41 79 21 82 33 77 37 62 51 0 65;
    74 51 42 51 58 37 37 33 46 21 65 0;
];

% 定义算法参数
maxIterations = 100;
maxNoImprove = 10;
destroyProb = 0.5;
repairProb = 0.5;

% 初始化解
currentSolution = [1:12];
bestSolution = currentSolution;
bestCost = inf;

% 迭代搜索
noImprovementCounter = 0;
for i = 1:maxIterations
    % 摧毁当前解
    candidateSolution = destroy(currentSolution, destroyProb);
    
    % 修复破坏的部分
    candidateSolution = repair(candidateSolution, repairProb, distMatrix);
    
    % 计算解的成本
    candidateCost = costFunction(candidateSolution, distMatrix);
    
    % 判断是否接受新解
    if candidateCost < bestCost
        bestSolution = candidateSolution;
        bestCost = candidateCost;
        noImprovementCounter = 0;
    else
        noImprovementCounter = noImprovementCounter + 1;
    end
    
    % 判断是否达到停止条件
    if noImprovementCounter >= maxNoImprove
        break;
    end
    
    % 更新当前解
    currentSolution = bestSolution;
end

% 输出最优解和成本
disp(bestSolution);
disp(bestCost);

% 定义摧毁函数
function candidateSolution = destroy(currentSolution, destroyProb)
    n = length(currentSolution);
    candidateSolution = currentSolution;
    for i = 1:n
        if rand() < destroyProb
            j = randi([1,n]);
            candidateSolution([i,j]) = candidateSolution([j,i]);
        end
    end
end

% 定义修复函数
function candidateSolution = repair(currentSolution, repairProb, distMatrix)
    n = length(currentSolution);
    candidateSolution = currentSolution;
    for i = 1:n
        if rand() < repairProb
            candidateSolution(i) = findBestInsertion(i, candidateSolution, distMatrix);
        end
    end
end

% 定义成本函数
function cost = costFunction(solution, distMatrix)
    n = length(solution);
    cost = 0;
    for i = 1:n-1
        cost = cost + distMatrix(solution(i), solution(i+1));
    end
    cost = cost + distMatrix(solution(n), solution(1));
end

% 定义最佳插入函数
function bestInsertion = findBestInsertion(city, solution, distMatrix)
    n = length(solution);
    bestCost = inf;
    bestInsertion = solution(1);
    for i = 1:n-1
        cost = distMatrix(solution(i), city) + distMatrix(city, solution(i+1)) - distMatrix(solution(i), solution(i+1));
        if cost < bestCost
            bestCost = cost;
            bestInsertion = i+1;
        end
    end
end

注意，这只是一个简单的示例代码，实际上，ALNS算法还需要进行更多的参数调整和优化才能得到更好的结果。