.csv数据中室外最高温,室外最低温,室内温度,室内人数作为输入,舒适度 = k1 × (室内温度 - 目标温度)² + k2 × (室外温度 - 室内温度)² + k3 × ln(室内人数)为目标函数,舒适度温度输出,利用灰狼优化算法进行参数寻优,python
时间: 2024-02-18 08:59:52 浏览: 78
python-温度监测数据(代码+数据)
好的,这是一个很有趣的问题。我可以帮助您实现这个目标函数和使用灰狼优化算法进行参数寻优的过程。以下是代码示例:
```python
import pandas as pd
import numpy as np
from math import log
# 读取数据
data = pd.read_csv('data.csv')
# 定义目标函数
def objective_function(x, data):
k1, k2, k3 = x
target_temp = 25 # 目标温度
temp_in = data['室内温度']
temp_out = data['室外温度']
num_people = data['室内人数']
discomfort = k1 * (temp_in - target_temp) ** 2 + k2 * (temp_out - temp_in) ** 2 + k3 * log(num_people)
return discomfort
# 灰狼优化算法
def gray_wolf_optimization(data, obj_func, num_vars, num_wolves=5, num_iterations=100, alpha=0.5):
# 初始化灰狼群
wolves = np.random.uniform(low=-1, high=1, size=(num_wolves, num_vars))
# 计算初始适应度
fitness = np.array([obj_func(wolf, data) for wolf in wolves])
# 迭代寻优
for i in range(num_iterations):
# 排序,找到最优和次优的灰狼
alpha_wolf = wolves[np.argmin(fitness)]
beta_wolf = wolves[np.argsort(fitness)[1]]
# 随机选择一只灰狼作为delta_wolf
delta_wolf = wolves[np.random.randint(num_wolves)]
# 更新每只灰狼的位置
for j in range(num_wolves):
r1, r2 = np.random.uniform(size=2)
A = 2 * alpha * r1 - alpha
C = 2 * r2
D = abs(C * delta_wolf - wolves[j])
X1 = delta_wolf - A * D
r1, r2 = np.random.uniform(size=2)
A = 2 * alpha * r1 - alpha
C = 2 * r2
D = abs(C * alpha_wolf - wolves[j])
X2 = alpha_wolf - A * D
r1, r2 = np.random.uniform(size=2)
A = 2 * alpha * r1 - alpha
C = 2 * r2
D = abs(C * beta_wolf - wolves[j])
X3 = beta_wolf - A * D
new_wolf = (X1 + X2 + X3) / 3
# 确保新位置在取值范围内
new_wolf = np.clip(new_wolf, -1, 1)
# 更新适应度
new_fitness = obj_func(new_wolf, data)
if new_fitness < fitness[j]:
wolves[j] = new_wolf
fitness[j] = new_fitness
# 返回最优解和最优适应度
best_wolf = wolves[np.argmin(fitness)]
best_fitness = obj_func(best_wolf, data)
return best_wolf, best_fitness
# 运行灰狼优化算法
best_wolf, best_fitness = gray_wolf_optimization(data, objective_function, 3)
# 输出最优解和最优适应度
print('最优解:', best_wolf)
print('最优适应度:', best_fitness)
```
需要注意的是,上述代码中的 `data.csv` 文件应该包含室外最高温、室外最低温、室内温度和室内人数这四列数据。此外,灰狼优化算法的参数 `num_wolves` 和 `num_iterations` 可以调整以获得更好的结果,但也可能需要更长的运行时间。
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