基于灰狼算法的储能选址定容代码
时间: 2024-03-28 13:41:43 浏览: 16
以下是基于灰狼算法的储能选址定容Python代码示例:
```python
import numpy as np
# 定义目标函数,其中x为储能站的选址和容量
def objective(x):
cost = # 储能系统建设和运行成本的总和
return cost
# 定义灰狼算法
def grey_wolf_optimizer(obj_func, lb, ub, dim, n, max_iter):
alpha_pos, alpha_score = None, float("inf") # 记录最优解和最优解的适应度值
beta_pos, beta_score = None, float("inf") # 记录次优解和次优解的适应度值
delta_pos, delta_score = None, float("inf") # 记录第三优解和第三优解的适应度值
# 初始化灰狼群体
x = np.random.uniform(lb, ub, (n, dim))
# 迭代寻优
for i in range(max_iter):
# 计算适应度值
scores = np.apply_along_axis(obj_func, 1, x)
# 更新最优解
best_idx = np.argmin(scores)
if scores[best_idx] < alpha_score:
alpha_pos = x[best_idx]
alpha_score = scores[best_idx]
# 更新次优解
if scores[best_idx] > alpha_score and scores[best_idx] < beta_score:
beta_pos = x[best_idx]
beta_score = scores[best_idx]
# 更新第三优解
if scores[best_idx] > alpha_score and scores[best_idx] > beta_score and scores[best_idx] < delta_score:
delta_pos = x[best_idx]
delta_score = scores[best_idx]
# 更新灰狼位置
a = 2 - 2 * i / max_iter # 控制参数a的变化
for j in range(n):
r1 = np.random.random(dim)
r2 = np.random.random(dim)
A = 2 * a * r1 - a
C = 2 * r2
D = np.abs(C * alpha_pos - x[j])
x1 = alpha_pos - A * D
r1 = np.random.random(dim)
r2 = np.random.random(dim)
A = 2 * a * r1 - a
C = 2 * r2
D = np.abs(C * beta_pos - x[j])
x2 = beta_pos - A * D
r1 = np.random.random(dim)
r2 = np.random.random(dim)
A = 2 * a * r1 - a
C = 2 * r2
D = np.abs(C * delta_pos - x[j])
x3 = delta_pos - A * D
x[j] = (x1 + x2 + x3) / 3
# 约束处理
x = np.clip(x, lb, ub)
return alpha_pos, alpha_score
# 定义问题参数和约束条件
lb = # 储能站选址下界
ub = # 储能站选址上界
dim = # 储能站选址和容量的维数
n = # 灰狼群体数量
max_iter = # 最大迭代次数
# 调用灰狼算法求解最优解
best_pos, best_score = grey_wolf_optimizer(objective, lb, ub, dim, n, max_iter)
```
需要根据实际问题设置目标函数、约束条件、参数等,具体实现时可以参考上述示例代码进行修改。