写一个不含numpy的蚁群算法解决TSP问题的代码
时间: 2023-06-13 17:05:08 浏览: 60
以下是一个不含numpy的蚁群算法解决TSP问题的Python代码:
```python
import random
# 定义城市数量和蚂蚁数量
num_cities = 10
num_ants = 5
# 定义城市坐标
cities = []
for i in range(num_cities):
cities.append((random.random(), random.random()))
# 定义距离矩阵
distances = []
for i in range(num_cities):
row = []
for j in range(num_cities):
dx = cities[i][0] - cities[j][0]
dy = cities[i][1] - cities[j][1]
dist = (dx*dx + dy*dy) ** 0.5
row.append(dist)
distances.append(row)
# 定义蚂蚁类
class Ant:
def __init__(self, alpha, beta):
self.alpha = alpha
self.beta = beta
self.visited = [False] * num_cities
self.tour = []
def select_next_city(self, pheromone):
# 计算每个城市的概率
probs = []
total = 0.0
current_city = self.tour[-1]
for i in range(num_cities):
if not self.visited[i]:
pher = pheromone[current_city][i]
dist = distances[current_city][i]
prob = (pher ** self.alpha) * ((1.0 / dist) ** self.beta)
probs.append((i, prob))
total += prob
# 根据概率选择下一个城市
r = random.uniform(0, total)
upto = 0.0
for i, prob in probs:
if upto + prob >= r:
return i
upto += prob
def tour_length(self):
total = 0.0
for i in range(num_cities):
j = (i + 1) % num_cities
city_i = self.tour[i]
city_j = self.tour[j]
dist = distances[city_i][city_j]
total += dist
return total
def tour_string(self):
s = ''
for i in range(num_cities):
s += str(self.tour[i]) + ' '
return s
def search(self, pheromone):
# 初始化蚂蚁的起点
start = random.randint(0, num_cities-1)
self.visited[start] = True
self.tour.append(start)
# 搜索路径
for i in range(num_cities-1):
next_city = self.select_next_city(pheromone)
self.visited[next_city] = True
self.tour.append(next_city)
def update_pheromone(self, pheromone, decay, quality):
# 计算路径长度
tour_length = self.tour_length()
# 更新信息素
for i in range(num_cities):
j = (i + 1) % num_cities
city_i = self.tour[i]
city_j = self.tour[j]
pheromone[city_i][city_j] *= decay
pheromone[city_i][city_j] += quality / tour_length
pheromone[city_j][city_i] = pheromone[city_i][city_j]
# 定义参数
alpha = 1.0
beta = 3.0
rho = 0.1
q = 1.0
# 初始化信息素矩阵
pheromone = []
for i in range(num_cities):
row = []
for j in range(num_cities):
row.append(1.0)
pheromone.append(row)
# 开始搜索
best_tour = None
best_length = float('inf')
for i in range(100):
# 初始化蚂蚁
ants = []
for j in range(num_ants):
ant = Ant(alpha, beta)
ants.append(ant)
# 蚂蚁搜索路径
for ant in ants:
ant.search(pheromone)
if ant.tour_length() < best_length:
best_tour = ant.tour
best_length = ant.tour_length()
# 更新信息素
for i in range(num_ants):
ants[i].update_pheromone(pheromone, rho, q / best_length)
# 输出结果
print('Iteration %d: Best Length = %.2f' % (i, best_length))
# 输出最优路径
print('Best Tour:', best_tour)
```
注:该代码仅供参考,可能不是最优实现,并且没有经过严格的测试和优化。
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