用matlab写一段配送路径优化的代码
时间: 2023-06-15 08:04:44 浏览: 160
以下是基于遗传算法的配送路径优化的 MATLAB 代码示例:
```matlab
% 配送点坐标
points = [0 0; 2 4; 3 5; 4 4; 5 6; 6 3; 7 7];
% 配送点数量
n = size(points, 1);
% 生成随机初始路径
initPath = randperm(n);
% 遗传算法参数
popSize = 20; % 种群大小
numGen = 100; % 迭代次数
mutationRate = 0.01; % 变异率
eliteRate = 0.1; % 精英率
% 初始化种群
pop = zeros(popSize, n);
for i = 1:popSize
pop(i,:) = randperm(n);
end
% 迭代
for gen = 1:numGen
% 计算适应度
fitness = zeros(popSize, 1);
for i = 1:popSize
fitness(i) = pathLength(points(pop(i,:), :));
end
% 选择
eliteNum = round(popSize * eliteRate);
[~, eliteIdx] = sort(fitness);
elitePop = pop(eliteIdx(1:eliteNum), :);
pop = selection(pop, fitness, popSize - eliteNum);
% 交叉
pop = crossover(pop, elitePop);
% 变异
pop = mutation(pop, mutationRate);
end
% 选出最佳路径
bestPath = pop(1,:);
bestLength = pathLength(points(bestPath, :));
% 显示最佳路径
disp('最佳路径:');
disp(bestPath);
disp(['最佳路径长度:', num2str(bestLength)]);
% 绘制路径图
figure;
plot(points(:,1), points(:,2), 'bo');
hold on;
plot(points(bestPath,1), points(bestPath,2), 'r-');
xlabel('x');
ylabel('y');
% 计算路径长度
function length = pathLength(points)
n = size(points, 1);
length = 0;
for i = 2:n
length = length + norm(points(i,:) - points(i-1,:));
end
length = length + norm(points(1,:) - points(n,:));
end
% 选择
function newPop = selection(pop, fitness, numNew)
newPop = zeros(numNew, size(pop,2));
totalFitness = sum(fitness);
for i = 1:numNew
r = rand * totalFitness;
j = 1;
while r > 0
r = r - fitness(j);
j = j + 1;
end
j = j - 1;
newPop(i,:) = pop(j,:);
end
end
% 交叉
function newPop = crossover(pop, elitePop)
popSize = size(pop, 1);
eliteSize = size(elitePop, 1);
newPop = zeros(popSize, size(pop,2));
for i = 1:popSize
idx1 = randi(eliteSize);
idx2 = randi(eliteSize);
parent1 = elitePop(idx1,:);
parent2 = elitePop(idx2,:);
child = zeros(1, size(pop,2));
startIdx = randi(size(pop,2));
endIdx = randi(size(pop,2));
if startIdx > endIdx
temp = startIdx;
startIdx = endIdx;
endIdx = temp;
end
child(startIdx:endIdx) = parent1(startIdx:endIdx);
for j = 1:size(pop,2)
if child(j) == 0
idx = find(parent2 == pop(i,j));
while child(idx) ~= 0
idx = find(parent2 == pop(i,idx));
end
child(idx) = pop(i,j);
end
end
newPop(i,:) = child;
end
end
% 变异
function newPop = mutation(pop, mutationRate)
popSize = size(pop, 1);
n = size(pop, 2);
numMutate = round(popSize * n * mutationRate);
newPop = pop;
for i = 1:numMutate
idx1 = randi(popSize);
idx2 = randi(n);
idx3 = randi(n);
temp = newPop(idx1, idx2);
newPop(idx1, idx2) = newPop(idx1, idx3);
newPop(idx1, idx3) = temp;
end
end
```
其中,`points`为配送点坐标矩阵,`pathLength`函数计算路径长度,`selection`函数进行选择操作,`crossover`函数进行交叉操作,`mutation`函数进行变异操作。在主程序中,先生成随机初始路径,再进行遗传算法迭代,最终输出最佳路径及长度,并绘制路径图。