用python写一个基于蚁群算法的路径规划的可视化代码
时间: 2024-05-11 07:17:03 浏览: 5
好的,下面是一个基于Python的蚁群算法路径规划的可视化代码示例:
```python
import numpy as np
import matplotlib.pyplot as plt
import random
# 蚂蚁数量
ant_num = 50
# 迭代次数
iteration_num = 100
# 蚁群信息素初始值
pheromone_init = 0.1
# 信息素挥发因子
evaporation_factor = 0.5
# 信息素增强因子
enhance_factor = 1
# 城市坐标
city_pos = np.array([[60, 200], [180, 200], [80, 180], [140, 180], [20, 160], [100, 160], [200, 160], [140, 140], [40, 120], [100, 120], [180, 100], [60, 80], [120, 80], [180, 60], [20, 40], [100, 40], [200, 40], [20, 20], [60, 20], [160, 20]])
# 城市个数
city_num = city_pos.shape[0]
# 距离矩阵
distance_matrix = np.zeros((city_num, city_num))
for i in range(city_num):
for j in range(city_num):
distance_matrix[i][j] = np.sqrt(np.sum((city_pos[i] - city_pos[j]) ** 2))
# 信息素矩阵
pheromone_matrix = np.ones((city_num, city_num)) * pheromone_init
# 最短路径长度
shortest_distance = np.inf
# 最短路径
shortest_path = []
# 迭代次数
for iteration in range(iteration_num):
# 当前迭代所有蚂蚁的路径
ant_path = np.zeros((ant_num, city_num), dtype=int)
# 当前迭代所有蚂蚁的路径长度
ant_distance = np.zeros(ant_num)
# 蚂蚁随机初始位置
ant_pos = np.zeros(ant_num, dtype=int)
# 每只蚂蚁依次选择下一个城市
for i in range(city_num - 1):
# 当前蚂蚁选择下一个城市的概率
select_prob = np.zeros((ant_num, city_num - i - 1))
for j in range(ant_num):
# 当前可选择的城市
available_city = np.delete(np.arange(city_num), ant_path[j][:i])
# 计算选择概率
select_prob[j] = np.power(pheromone_matrix[ant_pos[j]][available_city], enhance_factor) * np.power(1 / distance_matrix[ant_pos[j]][available_city], enhance_factor)
select_prob[j] /= np.sum(select_prob[j])
# 选择下一个城市
ant_path[j][i + 1] = available_city[np.random.choice(len(available_city), p=select_prob[j])]
# 计算路径长度
ant_distance[j] += distance_matrix[ant_path[j][i]][ant_path[j][i + 1]]
# 更新当前蚂蚁位置
ant_pos = ant_path[:, i + 1]
# 计算回到起点的路径长度
for j in range(ant_num):
ant_distance[j] += distance_matrix[ant_path[j][-1]][ant_path[j][0]]
# 更新最短路径
if ant_distance[j] < shortest_distance:
shortest_distance = ant_distance[j]
shortest_path = ant_path[j]
# 更新信息素
pheromone_matrix *= evaporation_factor
for j in range(ant_num):
for k in range(city_num - 1):
pheromone_matrix[ant_path[j][k]][ant_path[j][k + 1]] += (1 / ant_distance[j])
# 绘制当前迭代的最短路径
fig, ax = plt.subplots()
ax.plot(city_pos[:, 0], city_pos[:, 1], 'o')
for i in range(city_num - 1):
ax.plot(city_pos[[shortest_path[i], shortest_path[i + 1]], 0], city_pos[[shortest_path[i], shortest_path[i + 1]], 1], 'k')
ax.plot(city_pos[[shortest_path[-1], shortest_path[0]], 0], city_pos[[shortest_path[-1], shortest_path[0]], 1], 'k')
plt.show()
```
这个代码可以生成一个包含20个城市的路径规划问题,通过蚁群算法求解最短路径,并且每迭代一次就会绘制当前迭代的最短路径。你可以更改参数来改变城市数量、迭代次数等。