蚁群算法解决非对称TSP问题的python代码是什么
时间: 2024-03-06 15:48:54 浏览: 29
以下是使用蚁群算法解决非对称TSP问题的Python代码示例:
```python
import random
import numpy as np
# 非对称TSP问题的距离矩阵
distances = np.array([[0, 3, 4, 2],
[2, 0, 5, 6],
[4, 5, 0, 7],
[3, 2, 6, 0]])
# 蚂蚁数量
num_ants = 5
# 信息素初始值
pheromone = np.ones(distances.shape) / len(distances)
# 信息素挥发率
evaporation_rate = 0.5
# 信息素增强强度
alpha = 1
# 启发式信息强度
beta = 1
# 最大迭代次数
max_iterations = 100
# 记录每只蚂蚁走过的路径长度
ant_distances = np.zeros(num_ants)
# 记录最短路径长度和路径
best_distance = float('inf')
best_path = []
# 蚂蚁类
class Ant:
def __init__(self, start_city):
self.path = [start_city]
self.visited = set([start_city])
def choose_next_city(self):
current_city = self.path[-1]
unvisited_cities = set(range(len(distances))) - self.visited
if not unvisited_cities:
return None
probabilities = [pheromone[current_city][next_city]**alpha *
(1.0/distances[current_city][next_city])**beta
for next_city in unvisited_cities]
probabilities = np.array(probabilities) / sum(probabilities)
next_city = np.random.choice(list(unvisited_cities), p=probabilities)
self.path.append(next_city)
self.visited.add(next_city)
return next_city
def update_pheromone(self):
for i in range(len(self.path)-1):
current_city, next_city = self.path[i], self.path[i+1]
pheromone[current_city][next_city] += 1.0 / ant_distances[self.id]
# 初始化蚂蚁
ants = [Ant(random.randint(0, len(distances)-1)) for i in range(num_ants)]
# 迭代
for iteration in range(max_iterations):
# 蚂蚁走路
for ant in ants:
while ant.choose_next_city() is not None:
pass
ant_distances[ant.id] = sum(distances[ant.path[i]][ant.path[i+1]] for i in range(len(ant.path)-1))
if ant_distances[ant.id] < best_distance:
best_distance = ant_distances[ant.id]
best_path = ant.path
# 更新信息素
pheromone *= evaporation_rate
for ant in ants:
ant.update_pheromone()
# 重置蚂蚁
ants = [Ant(random.randint(0, len(distances)-1)) for i in range(num_ants)]
print('最短路径长度:', best_distance)
print('最短路径:', best_path)
```
在上述代码中,使用了一个 `Ant` 类来表示蚂蚁,每个蚂蚁都有一个 `path` 属性表示它所走过的路径,以及一个 `visited` 属性表示它所访问过的城市。在选择下一个城市时,蚂蚁会根据当前城市的信息素浓度和距离等因素计算每个未访问城市的选择概率,并以此来决定下一个城市。在走完一条路径后,蚂蚁会更新信息素浓度,使得走过更短路径的蚂蚁留下更多信息素,从而吸引其他蚂蚁跟随。最后,重复以上过程多次,直到找到最短路径。
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