写出基于灰狼优化算法的2维路径规划代码
时间: 2024-01-23 08:04:00 浏览: 41
以下是基于灰狼优化算法的2维路径规划代码,其中使用了matplotlib库绘制路径:
```python
import numpy as np
import matplotlib.pyplot as plt
# 灰狼优化算法
class GWO:
def __init__(self, search_func, dim, search_range, n_individuals=10, max_iter=100):
self.search_func = search_func
self.dim = dim
self.search_range = search_range
self.n_individuals = n_individuals
self.max_iter = max_iter
def optimize(self):
alpha_pos, beta_pos, delta_pos = self.init_individuals()
for t in range(self.max_iter):
a = 2 - 2 * t / self.max_iter # 控制参数a
for i in range(self.n_individuals):
# 灰狼位置更新
x = np.clip(alpha_pos + a * (beta_pos - delta_pos), self.search_range[0], self.search_range[1])
y = self.search_func(x)
fitness = self.fitness_func(y)
# 更新alpha, beta, delta
if fitness[i] < fitness[0]:
delta_pos[i] = beta_pos[i]
beta_pos[i] = alpha_pos[i]
alpha_pos[i] = x[i]
elif fitness[i] < fitness[1]:
delta_pos[i] = beta_pos[i]
beta_pos[i] = x[i]
elif fitness[i] < fitness[2]:
delta_pos[i] = x[i]
return alpha_pos
# 初始化灰狼群体
def init_individuals(self):
alpha_pos = np.zeros(self.dim)
beta_pos = np.zeros(self.dim)
delta_pos = np.zeros(self.dim)
fitness = np.zeros(self.n_individuals)
for i in range(self.dim):
alpha_pos[i] = np.random.uniform(self.search_range[0], self.search_range[1])
beta_pos[i] = np.random.uniform(self.search_range[0], self.search_range[1])
delta_pos[i] = np.random.uniform(self.search_range[0], self.search_range[1])
y_alpha = self.search_func(alpha_pos)
y_beta = self.search_func(beta_pos)
y_delta = self.search_func(delta_pos)
fitness[0] = self.fitness_func(y_alpha)
fitness[1] = self.fitness_func(y_beta)
fitness[2] = self.fitness_func(y_delta)
return alpha_pos, beta_pos, delta_pos
# 适应度函数
def fitness_func(self, y):
return 1 / (1 + y)
# 二维路径规划问题,目标函数为路径长度
def path_length(individual):
n = len(individual)
length = 0
for i in range(n-1):
length += np.sqrt((individual[i][0] - individual[i+1][0])**2 + (individual[i][1] - individual[i+1][1])**2)
return length
# 绘制路径
def plot_path(individual):
x = [point[0] for point in individual]
y = [point[1] for point in individual]
plt.plot(x, y, marker='o')
plt.show()
if __name__ == '__main__':
# 二维坐标范围
search_range = [-10, 10]
# 初始化路径
individual = np.array([[0, 0], [1, 2], [3, 1], [5, 4], [7, 5], [9, 3], [10, 10]])
# 初始化灰狼优化算法
gwo = GWO(path_length, len(individual), search_range, n_individuals=20, max_iter=100)
# 优化路径
optimized_path = gwo.optimize()
# 打印优化后的路径长度
print("优化后的路径长度为:", path_length(optimized_path))
# 绘制优化后的路径
plot_path(optimized_path)
```
注意:这里的二维路径规划问题是一个简单的示例,实际应用中可能需要根据具体情况进行修改。
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