商旅问题的路径优化代码

以下是一个基于贪心算法的商旅问题路径优化代码示例：

import numpy as np

def tsp(points):
    n = len(points)
    # 计算距离矩阵
    dist = np.zeros((n, n))
    for i in range(n):
        for j in range(i+1, n):
            dist[i][j] = dist[j][i] = np.linalg.norm(points[i]-points[j])
    # 初始化路径和距离
    path = [0]
    distance = 0
    # 贪心选择最近的下一个点
    while len(path) < n:
        current = path[-1]
        next_node = None
        min_dist = np.inf
        for i in range(n):
            if i not in path:
                if dist[current][i] < min_dist:
                    min_dist = dist[current][i]
                    next_node = i
        distance += min_dist
        path.append(next_node)
    # 回到起点
    distance += dist[path[-1]][0]
    path.append(0)
    return path, distance

# 测试
points = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
path, distance = tsp(points)
print('Path:', path)
print('Distance:', distance)

输出结果：

Path: [0, 1, 2, 3, 0]
Distance: 4.0

该代码使用numpy库计算距离矩阵，并使用贪心算法选择下一个最近的点，直到所有点都被遍历。最后将路径回到起点，并计算总距离。