用python模拟退火算法去解决TSP问题的代码
时间: 2024-01-05 09:03:16 浏览: 33
以下是用Python实现模拟退火算法解决TSP问题的示例代码:
```python
import random
import math
import sys
class TSP:
def __init__(self, filename):
self.nodes = []
self.distances = {}
self.read_file(filename)
def read_file(self, filename):
with open(filename, "r") as f:
for line in f:
x, y = map(float, line.split())
self.nodes.append((x, y))
def calc_distance(self, node1, node2):
return math.sqrt((node1[0] - node2[0]) ** 2 + (node1[1] - node2[1]) ** 2)
def calc_distances(self):
n = len(self.nodes)
for i in range(n):
for j in range(i + 1, n):
distance = self.calc_distance(self.nodes[i], self.nodes[j])
self.distances[(i, j)] = distance
self.distances[(j, i)] = distance
def get_distance(self, node1, node2):
if node1 > node2:
node1, node2 = node2, node1
return self.distances[(node1, node2)]
def get_path_distance(self, path):
distance = 0
for i in range(len(path)):
distance += self.get_distance(path[i], path[(i + 1) % len(path)])
return distance
class SimulatedAnnealing:
def __init__(self, tsp, initial_temp, cooling_rate, num_iterations):
self.tsp = tsp
self.initial_temp = initial_temp
self.cooling_rate = cooling_rate
self.num_iterations = num_iterations
self.current_path = list(range(len(self.tsp.nodes)))
self.best_path = self.current_path.copy()
self.temperature = self.initial_temp
def acceptance_probability(self, current_energy, new_energy, temperature):
if new_energy < current_energy:
return 1.0
else:
return math.exp((current_energy - new_energy) / temperature)
def run(self):
for i in range(self.num_iterations):
new_path = self.current_path.copy()
index1 = random.randint(0, len(self.current_path) - 1)
index2 = random.randint(0, len(self.current_path) - 1)
new_path[index1], new_path[index2] = new_path[index2], new_path[index1]
current_energy = self.tsp.get_path_distance(self.current_path)
new_energy = self.tsp.get_path_distance(new_path)
if self.acceptance_probability(current_energy, new_energy, self.temperature) > random.random():
self.current_path = new_path
if self.tsp.get_path_distance(self.current_path) < self.tsp.get_path_distance(self.best_path):
self.best_path = self.current_path.copy()
self.temperature *= self.cooling_rate
return self.best_path
if __name__ == "__main__":
tsp = TSP(sys.argv[1])
tsp.calc_distances()
sa = SimulatedAnnealing(tsp, 100000.0, 0.99, 10000)
best_path = sa.run()
print("Best path found: ", best_path)
print("Path distance: ", tsp.get_path_distance(best_path))
```
在此示例中,`TSP`类用于读取TSP问题的节点坐标,并计算节点之间的距离。`SimulatedAnnealing`类实现模拟退火算法,包括计算能量、接受概率和运行算法的主循环。最后,我们使用这些类来解决TSP问题,找到最短的路径并输出结果。
要运行脚本,请在命令行中输入以下命令:
```bash
python tsp.py <tsp_file>
```
其中 `<tsp_file>` 是包含节点坐标的文件的路径。
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