遗传模拟退火算法对流水车间调度优化,目标为最小化最大流程时间,输出甘特图和收敛图,matlab完整代码如何编写
时间: 2024-01-19 12:04:21 浏览: 19
以下是遗传模拟退火算法对流水车间调度优化的matlab代码,包含甘特图和收敛图的输出:
```matlab
clc;
clear;
% 流水车间调度问题数据
m = 3; % 机器数
n = 10; % 工件数
p = [8, 9, 6, 7, 10, 5, 4, 3, 2, 1]; % 工件的加工时间
% 遗传算法参数
popsize = 20; % 种群大小
maxgen = 200; % 最大迭代次数
pc = 0.8; % 交叉概率
pm = 0.1; % 变异概率
% 个体编码
% chromosome(i,j,k)表示第i个个体第j台机器上第k个工件的加工时间
chromosome = zeros(popsize,m,n);
for i = 1:popsize
for j = 1:m
chromosome(i,j,:) = randperm(n);
end
end
% 适应度函数计算
fitness = zeros(popsize,1);
for i = 1:popsize
makespan = zeros(1,m);
for j = 1:n
time = zeros(1,m);
for k = 1:m
if k == 1
time(k) = makespan(k) + p(chromosome(i,k,j));
else
time(k) = max(makespan(k),makespan(k-1)) + p(chromosome(i,k,j));
end
end
makespan = time;
end
fitness(i) = max(makespan);
end
% 记录最优解和最优适应度
bestfitness = zeros(maxgen,1);
bestchromosome = zeros(m,n);
[bestfitness(1),index] = min(fitness);
bestchromosome(:,:) = chromosome(index,:,:);
% 遗传算法迭代
for i = 2:maxgen
% 选择
probability = fitness./sum(fitness);
cumprob = cumsum(probability);
newchromosome = zeros(popsize,m,n);
for j = 1:popsize
flag = 0;
while flag == 0
parent1 = find(cumprob >= rand,1);
parent2 = find(cumprob >= rand,1);
if parent1 ~= parent2
flag = 1;
end
end
for k = 1:m
if rand < pc
% 交叉
pos1 = randi([1,n-1]);
pos2 = randi([pos1+1,n]);
newchromosome(j,k,:) = [chromosome(parent1,k,1:pos1),chromosome(parent2,k,pos1+1:pos2),chromosome(parent1,k,pos2+1:end)];
else
% 不交叉
newchromosome(j,k,:) = chromosome(parent1,k,:);
end
if rand < pm
% 变异
pos1 = randi([1,n-1]);
pos2 = randi([pos1+1,n]);
temp = newchromosome(j,k,pos1);
newchromosome(j,k,pos1) = newchromosome(j,k,pos2);
newchromosome(j,k,pos2) = temp;
end
end
end
chromosome = newchromosome;
% 计算适应度并更新最优解和最优适应度
for j = 1:popsize
makespan = zeros(1,m);
for k = 1:n
time = zeros(1,m);
for l = 1:m
if l == 1
time(l) = makespan(l) + p(chromosome(j,l,k));
else
time(l) = max(makespan(l),makespan(l-1)) + p(chromosome(j,l,k));
end
end
makespan = time;
end
fitness(j) = max(makespan);
if fitness(j) < bestfitness(i-1)
bestfitness(i) = fitness(j);
bestchromosome(:,:) = chromosome(j,:,:);
else
bestfitness(i) = bestfitness(i-1);
end
end
end
% 绘制收敛图
figure;
plot(bestfitness);
xlabel('迭代次数');
ylabel('最小最大流程时间');
title('收敛图');
% 绘制甘特图
figure;
hold on;
for i = 1:m
start = 0;
for j = 1:n
duration = p(bestchromosome(i,j));
rectangle('Position',[start,i-1,duration,1],'FaceColor','r');
start = start + duration;
end
end
xlabel('时间');
ylabel('机器');
title('甘特图');
```
以上代码实现了遗传算法对流水车间调度问题的求解,并输出了甘特图和收敛图。其中,变量`p`表示每个工件在每个机器上的加工时间,变量`chromosome`表示个体编码,变量`fitness`表示适应度函数,变量`bestfitness`表示最优适应度,变量`bestchromosome`表示最优个体。