tsp旅行商模拟退火算法python
时间: 2023-06-22 20:31:16 浏览: 47
这里是使用模拟退火算法解决TSP问题的Python代码:
```python
import numpy as np
import pandas as pd
import math
import random
import matplotlib.pyplot as plt
# 读取数据
df = pd.read_csv("data.tsp", sep=" ", skiprows=6, header=None)
city = np.array(range(1, len(df)+1))
city_x = np.array(df[1])
city_y = np.array(df[2])
# 计算两点之间的距离
def distance(city_x, city_y, i, j):
return math.sqrt((city_x[i]-city_x[j])**2 + (city_y[i]-city_y[j])**2)
# 计算路径长度
def path_length(city_x, city_y, path):
length = 0
for i in range(len(path)-1):
length += distance(city_x, city_y, path[i], path[i+1])
length += distance(city_x, city_y, path[len(path)-1], path[0])
return length
# 初始解
path = np.concatenate(([1], np.random.permutation(city[1:]), [1]))
# 初始温度、终止温度、温度衰减率、迭代次数
T0 = 100
T_end = 1e-3
alpha = 0.99
iter_num = 10000
# 记录搜索过程中最优解和对应的路径长度
best_path = path
best_length = path_length(city_x, city_y, path)
length_list = [best_length]
# 模拟退火算法
T = T0
iter_count = 0
while T > T_end and iter_count < iter_num:
i, j = sorted(np.random.choice(city, size=2, replace=False))
new_path = np.concatenate((path[:i], np.flip(path[i:j+1]), path[j+1:]))
new_length = path_length(city_x, city_y, new_path)
delta = new_length - best_length
if delta < 0 or math.exp(-delta/T) > random.uniform(0, 1):
path = new_path
best_length = new_length
if new_length < length_list[-1]:
best_path = new_path
length_list.append(new_length)
T *= alpha
iter_count += 1
# 打印最优路径长度和路径
print("Best path length: ", best_length)
print("Best path: ", best_path)
# 可视化最优路径
plt.plot(city_x[best_path], city_y[best_path], 'o-')
plt.title("TSP Solution with Simulated Annealing")
plt.show()
```
需要注意的是,读取数据和计算路径长度的代码需要根据实际情况进行修改。同时,我们也可以通过调整初始温度、终止温度、温度衰减率和迭代次数来控制算法的搜索范围和搜索速度。