att48用模拟退火算法的完整、python代码
时间: 2024-04-26 16:25:12 浏览: 9
以下是使用模拟退火算法求解ATT48问题的Python代码:
```python
import math
import random
def get_distance(coord1, coord2):
"""
计算两个坐标之间的欧几里得距离
"""
return math.sqrt((coord1[0] - coord2[0]) ** 2 + (coord1[1] - coord2[1]) ** 2)
def get_total_distance(coords, order):
"""
计算一条路径的总距离
"""
dist = 0
for i in range(len(order)):
dist += get_distance(coords[order[i]], coords[order[(i + 1) % len(order)]])
return dist
def get_neighbor(order):
"""
生成一个随机的邻居
"""
i = random.randint(0, len(order) - 1)
j = random.randint(0, len(order) - 1)
neighbor = order[:]
neighbor[i], neighbor[j] = neighbor[j], neighbor[i]
return neighbor
def simulated_annealing(coords, order, max_iter=10000, init_temperature=100, cooling_rate=0.99):
"""
模拟退火算法求解TSP问题
"""
temperature = init_temperature
current_order = order[:]
current_dist = get_total_distance(coords, current_order)
best_order = current_order[:]
best_dist = current_dist
for i in range(max_iter):
neighbor_order = get_neighbor(current_order)
neighbor_dist = get_total_distance(coords, neighbor_order)
delta = neighbor_dist - current_dist
if delta < 0 or math.exp(-delta / temperature) > random.uniform(0, 1):
current_order, current_dist = neighbor_order, neighbor_dist
if current_dist < best_dist:
best_order, best_dist = current_order[:], current_dist
temperature *= cooling_rate
return best_order, best_dist
if __name__ == '__main__':
# 读入坐标数据
coords = []
with open('att48.tsp') as f:
for line in f.readlines()[6:]:
x, y = map(float, line.strip().split()[1:])
coords.append((x, y))
# 生成随机初始序列
order = list(range(len(coords)))
random.shuffle(order)
# 使用模拟退火算法求解TSP问题
best_order, best_dist = simulated_annealing(coords, order)
# 输出结果
print('最短距离:', best_dist)
print('最短路径:', best_order)
```
需要注意的是,上述代码需要读入一个TSP问题的坐标数据,可以从http://elib.zib.de/pub/mp-testdata/tsp/tsplib/tsp/att48.tsp 下载数据文件,并将其放在代码所在目录。另外,模拟退火算法的求解结果可能受到算法参数的影响,需要进行适当的调参才能得到较好的结果。