退火法基于att48数据集求解最优解python代码
时间: 2024-02-03 13:04:33 浏览: 48
以下是使用退火算法求解att48数据集的TSP问题的Python代码:
``` python
import random
import math
import pandas as pd
# 读取数据集
def read_data():
data = pd.read_csv('att48.csv', header=None, index_col=0).values
cities = {}
for i in range(len(data)):
cities[i] = (data[i][0], data[i][1])
return cities
# 计算路径长度
def distance(path, cities):
distance = 0
for i in range(len(path) - 1):
distance += math.sqrt((cities[path[i]][0] - cities[path[i+1]][0])**2 +
(cities[path[i]][1] - cities[path[i+1]][1])**2)
return distance
# 交换路径中两个城市的位置
def swap(path, i, j):
path[i], path[j] = path[j], path[i]
return path
# 退火算法求解TSP问题
def tsp(cities, T, cooling_rate):
# 初始化路径
path = list(range(len(cities)))
random.shuffle(path)
best_path = path
# 初始温度
temp = T
# 循环直到温度降至停止温度
while temp > 1:
# 随机交换两个城市的位置,得到新路径
i, j = random.sample(range(len(cities)), 2)
new_path = swap(path, i, j)
# 计算新路径的长度
new_distance = distance(new_path, cities)
old_distance = distance(path, cities)
# 计算接受新路径的概率
accept_prob = math.exp(-(new_distance - old_distance) / temp)
# 根据概率接受或拒绝新路径
if accept_prob > random.random():
path = new_path
# 更新最优路径
if distance(path, cities) < distance(best_path, cities):
best_path = path
# 降温
temp *= cooling_rate
return best_path
# 主函数
def main():
cities = read_data()
best_path = tsp(cities, 1000, 0.99)
best_distance = distance(best_path, cities)
print('Best Path:', best_path)
print('Best Distance:', best_distance)
if __name__ == '__main__':
main()
```
其中,att48.csv是att48数据集的文件,cities是一个字典,表示每个城市的坐标;T是初始温度;cooling_rate是温度下降系数。使用该函数求解att48数据集的TSP问题,输出的是最优路径和最优距离。