如何生成下面这段代码的成本迭代图，%% 用户自定义数据 [customerPosition,distriCenterPosition,sendPosition,distriCenterConstantCost,distriCenterVolume,... distriCenterManageCost,sendToDistriCenterPer,distriCenterToCustomerPer,customerRequirement] = DataFunction; probabilityMat = [0,0,0,0,0,1,0,0,0,0,0,0,0,0,0]; % probabilityMat = ones(1,7)/7; % probabilityMat = [0 0 0 1 0 0 0]; %% 用户不可修改数据 M = size(distriCenterPosition,1); %配送中心的数目 N = size(customerPosition,1); %客户的数目 if length(probabilityMat)~=M || abs(sum(probabilityMat)-1)>=1e-6 error('参数probabilityMat的大小与配送中心数目不一致或probabilityMat加和不为1'); end %% 遗传算法数据 populationNumber = 50; %种群数量 pro = 0.1; %变异概率 populationMat = cell(populationNumber,4); %种群数据矩阵 iter = 1; %当前迭代次数 iterMax = 3000; % 最大迭代次数 bestValue = inf; bestValueNow = inf; bestValueMat = zeros(populationNumber,1); bestSolve = cell(1,4); %% 初始化个体 populationMat = InitializeIndividuality(populationMat,populationNumber,M,N,probabilityMat,customerRequirement,distriCenterVolume,distriCenterPosition,customerPosition); while iter<=iterMax if iter<iterMax/2 pro = 0.5; else pro = 0.05; end %% 计算适应度 p = CalcuFitCapacity(populationMat,sendPosition,distriCenterPosition,sendToDistriCenterPer,customerPosition,distriCenterToCustomerPer,distriCenterConstantCost,distriCenterManageCost,populationNumber); %% 选择 populationMat = SelectionFunction(populationMat,populationNumber,p); %% 交叉 populationMat = CrossFunction(populationMat,populationNumber,customerRequirement,distriCenterVolume,distriCenterPosition,customerPosition,N); %% 变异 populationMat = HeteromorphosisFunction(populationMat,probabilityMat,pro,populationNumber,M,N,customerRequirement,distriCenterVolume,distriCenterPosition,customerPosition); %% 寻找最优解 [bestValueNow,bestSolveNow] = CalcuBestIndividuiality(populationMat,populationNumber,sendPosition,distriCenterPosition,sendToDistriCenterPer,customerPosition,distriCenterToCustomerPer,distriCenterConstantCost,distriCenterManageCost); if bestValueNow<bestValue bestValueMat(iter) = bestValueNow; bestValue = bestValueNow; bestSolve = bestSolveNow; else bestValueMat(iter) = bestValue; end iter = iter+1;

您可以使用 MATLAB 自带的 plot 函数来绘制成本迭代图。具体的步骤如下：

1. 定义迭代次数和最优解的数组：

iterMax = 3000;
bestValueMat = zeros(iterMax, 1);

2. 在循环中记录每次迭代的最优解：

for iter = 1:iterMax
    ...
    [bestValueNow, bestSolveNow] = CalcuBestIndividuiality(populationMat,populationNumber,sendPosition,distriCenterPosition,sendToDistriCenterPer,customerPosition,distriCenterToCustomerPer,distriCenterConstantCost,distriCenterManageCost);
    if bestValueNow < bestValue
        bestValue = bestValueNow;
        bestSolve = bestSolveNow;
    end
    bestValueMat(iter) = bestValue;
    iter = iter + 1;
end

3. 使用 plot 函数绘制成本迭代图：

plot(1:iterMax, bestValueMat);
title('Cost Iteration Plot');
xlabel('Iteration');
ylabel('Cost');

这样就可以生成成本迭代图了。您可以将这段代码放在 ChitGPT 中执行，然后与 ChitGPT 对话，让它为您生成成本迭代图。