matlab实现：C是空间城市三维坐标，D是距离矩阵，遗传算法解决TSP问题，要求迭代绘制每一次的最优路径，注意坐标是三维的

好的，这是一个比较典型的TSP问题，使用遗传算法可以得到较优的解。下面是MATLAB实现的代码：

% 定义城市坐标
C = [1 2 3; 4 5 6; 7 8 9; 10 11 12; 13 14 15];

% 计算距离矩阵
n = size(C, 1); % 城市数量
D = zeros(n, n);
for i = 1:n
    for j = 1:n
        D(i, j) = norm(C(i,:) - C(j,:)); % 计算欧几里得距离
    end
end

% 设置遗传算法参数
popSize = 50; % 种群大小
numGen = 100; % 迭代次数
numElite = 2; % 精英个体数量
mutProb = 0.01; % 变异概率

% 初始化种群
pop = zeros(popSize, n);
for i = 1:popSize
    pop(i,:) = randperm(n);
end

% 计算适应度
fitness = zeros(popSize, 1);
for i = 1:popSize
    fitness(i) = calcFitness(pop(i,:), D);
end

% 记录每一代的最优解
bestFitness = zeros(numGen, 1);
bestPath = zeros(numGen, n);
[bestFitness(1), idx] = min(fitness);
bestPath(1,:) = pop(idx,:);

% 迭代
for gen = 2:numGen
    % 选择
    parents = selection(pop, fitness, numElite);
    
    % 交叉
    offspring = crossover(parents, popSize);
    
    % 变异
    offspring = mutation(offspring, mutProb);
    
    % 计算适应度
    fitness = zeros(popSize, 1);
    for i = 1:popSize
        fitness(i) = calcFitness(offspring(i,:), D);
    end
    
    % 合并种群
    pop = [parents; offspring];
    fitness = [fitness; fitness];
    
    % 保留精英个体
    [fitness, idx] = sort(fitness);
    pop = pop(idx,:);
    pop = pop(1:popSize,:);
    fitness = fitness(1:popSize);
    
    % 记录最优解
    [bestFitness(gen), idx] = min(fitness);
    bestPath(gen,:) = pop(idx,:);
    
    % 绘制最优路径
    plot3(C(:,1), C(:,2), C(:,3), 'ro');
    hold on;
    plot3(C([bestPath(gen,:) bestPath(gen,1)],1), C([bestPath(gen,:) bestPath(gen,1)],2), C([bestPath(gen,:) bestPath(gen,1)],3), 'b--');
    hold off;
    drawnow;
end

% 绘制最优路径
plot3(C(:,1), C(:,2), C(:,3), 'ro');
hold on;
plot3(C([bestPath(end,:) bestPath(end,1)],1), C([bestPath(end,:) bestPath(end,1)],2), C([bestPath(end,:) bestPath(end,1)],3), 'b--');
hold off;

% 计算路径长度
function f = calcFitness(path, D)
    f = 0;
    n = length(path);
    for i = 1:n-1
        f = f + D(path(i), path(i+1));
    end
    f = f + D(path(n), path(1));
end

% 选择
function parents = selection(pop, fitness, numElite)
    [~, idx] = sort(fitness);
    parents = pop(idx(1:numElite),:);
end

% 交叉
function offspring = crossover(parents, popSize)
    n = size(parents, 1);
    offspring = zeros(popSize-n, size(parents, 2));
    for i = 1:popSize-n
        % 随机选择两个父代个体
        idx1 = randi(n);
        idx2 = randi(n);
        while idx2 == idx1
            idx2 = randi(n);
        end
        parent1 = parents(idx1,:);
        parent2 = parents(idx2,:);
        
        % 随机选择交叉点
        pt = randi(length(parent1)-1);
        
        % 交叉
        offspring(i,:) = [parent1(1:pt) parent2(pt+1:end)];
    end
end

% 变异
function offspring = mutation(pop, mutProb)
    offspring = pop;
    for i = 1:size(offspring, 1)
        if rand < mutProb
            % 随机选择两个位置
            idx1 = randi(size(offspring, 2));
            idx2 = randi(size(offspring, 2));
            while idx2 == idx1
                idx2 = randi(size(offspring, 2));
            end
            
            % 交换两个位置的值
            temp = offspring(i,idx1);
            offspring(i,idx1) = offspring(i,idx2);
            offspring(i,idx2) = temp;
        end
    end
end

以上代码使用了三维坐标来定义城市，实现了迭代绘制每一次的最优路径。在主函数中，首先计算了城市之间的