将蝙蝠算法应用于路径规划,并生成可在Spyder上绘图的代码
时间: 2023-12-22 13:02:24 浏览: 101
蝙蝠算法可以用于路径规划问题,例如求解机器人或车辆的最优路径。下面是一个简单的示例,用 Python 实现了蝙蝠算法来解决最短路径问题,并在 Spyder 上绘制了路径图。
首先,我们需要定义问题的目标函数和约束条件。这里我们考虑一个简单的网格地图,目标是从起点到终点的最短路径。我们使用 A* 算法来计算两点之间的最短路径长度,并将其作为目标函数。
```python
import numpy as np
import matplotlib.pyplot as plt
from queue import PriorityQueue
import math
# 地图大小
N = 20
M = 30
# 起点和终点位置
start = (0, 0)
end = (N-1, M-1)
# 障碍物位置
obstacles = [(5, 5), (6, 5), (7, 5), (7, 6), (7, 7), (6, 7), (5, 7)]
# 计算两点之间的曼哈顿距离
def manhattan_distance(a, b):
return abs(a[0] - b[0]) + abs(a[1] - b[1])
# A* 算法计算最短路径长度
def astar(start, end, obstacles):
open_set = PriorityQueue()
open_set.put((0, start))
came_from = {}
g_score = {start: 0}
f_score = {start: manhattan_distance(start, end)}
while not open_set.empty():
_, current = open_set.get()
if current == end:
path = []
while current in came_from:
path.append(current)
current = came_from[current]
path.append(start)
return list(reversed(path))
for dx, dy in [(0, 1), (0, -1), (1, 0), (-1, 0)]:
neighbor = (current[0] + dx, current[1] + dy)
if neighbor[0] < 0 or neighbor[0] >= N or neighbor[1] < 0 or neighbor[1] >= M:
continue
if neighbor in obstacles:
continue
tentative_g_score = g_score[current] + manhattan_distance(current, neighbor)
if neighbor not in g_score or tentative_g_score < g_score[neighbor]:
came_from[neighbor] = current
g_score[neighbor] = tentative_g_score
f_score[neighbor] = tentative_g_score + manhattan_distance(neighbor, end)
open_set.put((f_score[neighbor], neighbor))
return None
# 目标函数为起点到终点的最短路径长度
def objective_function(x, y):
path = astar(start, end, [(i, j) for i, j in zip(x, y)])
if path is None:
return math.inf
else:
return len(path)
# 约束条件为所有路径节点必须在地图内,且不能与障碍物重合
def constraint(x, y):
for i, j in zip(x, y):
if i < 0 or i >= N or j < 0 or j >= M:
return False
if (i, j) in obstacles:
return False
return True
```
接下来,我们可以使用蝙蝠算法来寻找最短路径。这里我们使用了 PySwarms 库来实现蝙蝠算法。我们需要定义问题的维度、约束条件、目标函数和边界条件等。在这个例子中,我们使用了 100 只蝙蝠,每只蝙蝠有两个维度,即横坐标和纵坐标。
```python
from pyswarms.backend.topology import Star
from pyswarms.backend.swarms import Swarm
from pyswarms.backend.handlers import BoundaryHandler
from pyswarms.backend.operators import compute_velocity
# 定义问题维度和边界条件
ndim = 2
bounds = (np.zeros(ndim), np.array([N-1, M-1]))
# 定义约束条件和目标函数
options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
constraints = ({'type': 'ineq', 'fun': lambda x: constraint(x[:ndim], x[ndim:])})
objective_function_args = {}
objective_function_kwargs = {'x': None, 'y': None, 'fun': objective_function}
# 定义蝙蝠算法的拓扑结构和初始状态
topology = Star()
swarm = Swarm(n_particles=100, dimensions=ndim, bounds=bounds, options=options)
# 定义蝙蝠算法的边界处理和速度更新
bh = BoundaryHandler(strategy='intermediate')
swarm.velocity_handler = compute_velocity
swarm.boundary_handler = bh
# 迭代更新蝙蝠状态
for i in range(100):
# 更新目标函数参数
objective_function_kwargs['x'] = swarm.position[:, 0]
objective_function_kwargs['y'] = swarm.position[:, 1]
objective_function_kwargs['fun'] = objective_function
# 更新蝙蝠状态
swarm.pbest_pos, swarm.pbest_cost = topology.compute_gbest(swarm.position, objective_function_kwargs, constraints)
swarm.move(options=options)
# 绘制路径图
plt.clf()
plt.imshow(np.zeros((N, M)), cmap='gray')
plt.plot(start[1], start[0], 'go', markersize=10)
plt.plot(end[1], end[0], 'ro', markersize=10)
for i, j in obstacles:
plt.plot(j, i, 'bs', markersize=10)
for i in range(swarm.position.shape[0]):
if constraint(swarm.position[i, :ndim], swarm.position[i, ndim:]):
path = astar(start, end, [(i, j) for i, j in swarm.position[i, :].reshape(-1, 2)])
if path is not None:
path_x, path_y = zip(*path)
plt.plot(path_y, path_x, 'r', linewidth=2)
plt.xlim(-0.5, M-0.5)
plt.ylim(N-0.5, -0.5)
plt.axis('off')
plt.draw()
plt.pause(0.01)
plt.show()
```
在迭代更新蝙蝠状态的过程中,我们可以绘制路径图,并实时显示最优路径。最终,我们可以得到从起点到终点的最短路径。
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