将python的循环输出结果转为csv文件
时间: 2023-09-29 11:03:08 浏览: 82
以下是一个简单的蚁群算法的Python代码示例:
```python
import random
# 初始化城市位置和信息素
def init():
global city, num_city, pheromone, num_ant, alpha, beta, Q
city = [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7), (8, 8), (9, 9)]
num_city = len(city)
pheromone = [[1.0 for j in range(num_city)] for i in range(num_city)]
num_ant = 5
alpha = 1.0
beta = 5.0
Q = 100.0
# 计算两个城市之间的距离
def distance(city1, city2):
return ((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2) ** 0.5
# 蚂蚁类
class Ant:
def __init__(self, start_city):
self.path = [start_city]
self.visited = [False for i in range(num_city)]
self.visited[start_city] = True
self.total_distance = 0.0
# 选择下一个城市
def select_next_city(self):
# 计算每个城市的概率
prob = [0.0 for i in range(num_city)]
for i in range(num_city):
if not self.visited[i]:
prob[i] = pheromone[self.path[-1]][i] ** alpha * (1.0 / distance(city[self.path[-1]], city[i])) ** beta
# 选择概率最高的城市
total_prob = sum(prob)
if total_prob > 0.0:
next_city = -1
rand = random.uniform(0.0, total_prob)
for i in range(num_city):
if not self.visited[i]:
rand -= prob[i]
if rand < 0.0:
next_city = i
break
if next_city != -1:
self.path.append(next_city)
self.visited[next_city] = True
self.total_distance += distance(city[self.path[-2]], city[self.path[-1]])
return True
return False
# 更新信息素
def update_pheromone(self):
for i in range(num_city - 1):
pheromone[self.path[i]][self.path[i+1]] += Q / self.total_distance
# 执行蚁群算法
def run():
init()
best_distance = float('inf')
best_path = []
for iter in range(100):
ants = [Ant(random.randint(0, num_city-1)) for i in range(num_ant)]
for ant in ants:
while ant.select_next_city():
pass
if ant.total_distance < best_distance:
best_distance = ant.total_distance
best_path = ant.path
for ant in ants:
ant.update_pheromone()
# 信息素挥发
for i in range(num_city):
for j in range(num_city):
pheromone[i][j] *= 0.9
return best_path, best_distance
# 测试
best_path, best_distance = run()
print('Best path:', [city[i] for i in best_path])
print('Best distance:', best_distance)
```
在这个示例中,我们定义了一个10个城市的旅行商问题,并使用蚁群算法来求解最短路径。首先,我们初始化城市位置和信息素,然后定义了一个计算两个城市之间距离的函数。接下来,我们定义了一个Ant类,它包含了选择下一个城市和更新信息素的方法。然后我们执行蚁群算法,每次迭代使用多个蚂蚁来寻找最优路径,并更新信息素。最后,我们输出最优路径和最短距离。
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