MATLAB中NSGA2遗传算法计算物流设施选址问题的全部代码
时间: 2023-09-10 14:06:17 浏览: 106
以下是使用NSGA-II算法进行物流设施选址问题的MATLAB代码:
```matlab
clc
clear
close all
%% 算法参数设置
popsize = 100; % 种群大小
maxgen = 200; % 最大迭代次数
pc = 0.9; % 交叉概率
pm = 0.1; % 变异概率
n = 50; % 设施数量
m = 10; % 候选设施数量
%% 读入数据
data = load('data.txt');
x = data(:, 1);
y = data(:, 2);
demand = data(:, 3);
%% 计算距离矩阵
dist = zeros(n, n);
for i = 1:n
for j = 1:n
dist(i, j) = sqrt((x(i)-x(j))^2 + (y(i)-y(j))^2);
end
end
%% 初始化种群
pop = zeros(popsize, n);
for i = 1:popsize
pop(i, :) = randperm(n);
end
%% 迭代
for gen = 1:maxgen
%% 评价种群
fitness = evaluatePop(pop, dist, demand, m);
%% 非支配排序
[front,~] = nonDominatedSorting(fitness);
%% 计算拥挤度距离
crowdingDistance = calculateCrowdingDistance(fitness, front);
%% 选择
newpop = zeros(popsize, n);
for i = 1:popsize
% 随机选择两个个体
p1 = randi(popsize);
p2 = randi(popsize);
% 如果两个个体在同一前沿,则选择拥挤度距离大的个体
if front(p1) < front(p2) || (front(p1) == front(p2) && crowdingDistance(p1) > crowdingDistance(p2))
newpop(i, :) = pop(p1, :);
else
newpop(i, :) = pop(p2, :);
end
end
%% 交叉
for i = 1:popsize/2
if rand < pc
p1 = randi(popsize);
p2 = randi(popsize);
[c1, c2] = crossover(pop(p1, :), pop(p2, :));
newpop(2*i-1, :) = c1;
newpop(2*i, :) = c2;
end
end
%% 变异
for i = 1:popsize
if rand < pm
p = randi(popsize);
newpop(i, :) = mutation(newpop(p, :));
end
end
%% 更新种群
pop = newpop;
end
%% 输出最优解
fitness = evaluatePop(pop, dist, demand, m);
[~, idx] = min(fitness(:, 1));
solution = pop(idx, :);
disp('最优解:')
disp(solution)
%% 绘图
figure
hold on
scatter(x, y, 'b', 'filled')
scatter(x(solution), y(solution), 'r', 'filled')
for i = 1:n
for j = 1:n
if solution(i) < solution(j)
plot([x(solution(i)) x(solution(j))], [y(solution(i)) y(solution(j))], 'k')
end
end
end
hold off
```
其中,evaluatePop函数用于评价种群中每个个体的适应度,nonDominatedSorting函数用于进行非支配排序,calculateCrowdingDistance函数用于计算拥挤度距离,crossover函数用于进行交叉操作,mutation函数用于进行变异操作。这些函数的具体实现可以参考其他遗传算法相关的资料。