写基于灰狼优化算法搜索边坡最危险滑面代码
时间: 2023-06-30 20:13:34 浏览: 52
以下是基于灰狼优化算法搜索边坡最危险滑面的Python代码示例:
```
import numpy as np
# 目标函数(最大剪应力)
def shear_stress(x, y):
return 2 * x * y / (x + y)
# 灰狼优化算法
def wolf_optimization_algorithm(target_func, search_range, wolf_num, iter_num):
# 初始化灰狼种群
wolves = np.random.uniform(search_range[0], search_range[1], size=(wolf_num, 2))
best_wolf = np.zeros(2)
best_fitness = float('-inf')
for i in range(iter_num):
# 计算适应度值
fitness = np.array([target_func(wolves[j, 0], wolves[j, 1]) for j in range(wolf_num)])
# 确定灰狼的等级和距离
alpha_index = np.argmax(fitness)
alpha_wolf = wolves[alpha_index]
for j in range(wolf_num):
A = 2 * (j + 1) / (2 * iter_num) # 灰狼位置更新的系数A
r1 = np.random.rand() # 随机数r1
C1 = 2 * r1 # 系数C1
D_alpha = np.abs(C1 * alpha_wolf - wolves[j])
X1 = alpha_wolf - A * D_alpha
# 更新最好的灰狼
if target_func(X1[0], X1[1]) > best_fitness:
best_wolf = X1
best_fitness = target_func(X1[0], X1[1])
beta_index = np.argsort(fitness)[-2]
beta_wolf = wolves[beta_index]
for k in range(wolf_num):
r2 = np.random.rand() # 随机数r2
C2 = 2 * r2 # 系数C2
D_beta = np.abs(C2 * beta_wolf - wolves[k])
X2 = beta_wolf - A * D_beta
delta_index = np.argsort(fitness)[-3]
delta_wolf = wolves[delta_index]
r3 = np.random.rand() # 随机数r3
C3 = 2 * r3 # 系数C3
D_delta = np.abs(C3 * delta_wolf - wolves[k])
X3 = delta_wolf - A * D_delta
# 更新灰狼位置
wolves[k] = (X1 + X2 + X3) / 3
return best_wolf
# 搜索边坡最危险滑面
search_range = [1, 10] # 搜索空间
wolf_num = 10 # 灰狼个数
iter_num = 100 # 迭代次数
best_wolf = wolf_optimization_algorithm(shear_stress, search_range, wolf_num, iter_num)
print("边坡最危险滑面的位置:", best_wolf)
print("最大剪应力:", shear_stress(best_wolf[0], best_wolf[1]))
```
在上述代码中,目标函数为最大剪应力,搜索空间为1到10,灰狼个数为10,迭代次数为100。最终输出边坡最危险滑面的位置和最大剪应力。需要注意的是,这只是一个简单的示例,实际情况下需要根据具体问题进行适当修改和调整。
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