禁忌搜索算法的matlab程序代码

禁忌搜索算法的Matlab程序代码可以根据算法的流程图和实现原理进行编写。以下是一个简单的禁忌搜索算法的Matlab程序代码示例：

function [bestSolution, bestCost] = tabuSearch(initialSolution, tabuListSize, maxIterations)
    currentSolution = initialSolution;
    bestSolution = currentSolution;
    bestCost = calculateCost(currentSolution);
    tabuList = zeros(tabuListSize, size(initialSolution, 2));
    
    for iteration = 1:maxIterations
        candidateSolutions = generateCandidateSolutions(currentSolution);
        candidateCosts = zeros(size(candidateSolutions, 1), 1);
        
        for i = 1:size(candidateSolutions, 1)
            if ~isTabu(candidateSolutions(i, :), tabuList)
                candidateCosts(i) = calculateCost(candidateSolutions(i, :));
            else
                candidateCosts(i) = inf;
            end
        end
        
        [minCost, minIndex] = min(candidateCosts);
        currentSolution = candidateSolutions(minIndex, :);
        
        if minCost < bestCost
            bestSolution = currentSolution;
            bestCost = minCost;
        end
        
        tabuList = updateTabuList(tabuList, currentSolution);
    end
end

function cost = calculateCost(solution)
    % 计算解的成本
    % TODO: 根据具体问题定义成本函数
end

function candidateSolutions = generateCandidateSolutions(solution)
    % 生成候选解
    % TODO: 根据具体问题生成候选解
end

function isTabu = isTabu(solution, tabuList)
    % 判断解是否在禁忌表中
    isTabu = any(ismember(tabuList, solution, 'rows'));
end

function updatedTabuList = updateTabuList(tabuList, solution)
    % 更新禁忌表
    updatedTabuList = circshift(tabuList, [1, 0]);
    updatedTabuList(1, :) = solution;
end

请注意，这只是一个简单的示例代码，具体的禁忌搜索算法的实现需要根据具体问题进行调整和优化。在实际应用中，您需要根据问题的特点和要求来定义成本函数、生成候选解的方法以及禁忌表的更新策略。