智能优化算法的38个测试集,用python编写
时间: 2023-08-04 16:18:16 浏览: 43
对于智能优化算法,常用的测试集有很多,以下是一些比较经典的测试集及其Python代码实现:
1. Sphere Function(球面函数):
```python
import numpy as np
def sphere(x):
return sum(np.power(x, 2))
```
2. Schwefel Function(Schwefel函数):
```python
import numpy as np
def schwefel(x):
return 418.9829 * len(x) - sum(x * np.sin(np.sqrt(np.abs(x))))
```
3. Rosenbrock Function(Rosenbrock函数):
```python
import numpy as np
def rosenbrock(x):
return sum(100.0 * (x[1:] - x[:-1] ** 2.0) ** 2.0 + (1 - x[:-1]) ** 2.0)
```
4. Rastrigin Function(Rastrigin函数):
```python
import numpy as np
def rastrigin(x):
A = 10
return A * len(x) + sum(x ** 2 - A * np.cos(2 * np.pi * x))
```
5. Griewank Function(Griewank函数):
```python
import numpy as np
def griewank(x):
return 1 + sum(x ** 2) / 4000.0 - np.prod(np.cos(x / np.sqrt(np.arange(1, len(x) + 1))))
```
6. Ackley Function(Ackley函数):
```python
import numpy as np
def ackley(x):
a = 20
b = 0.2
c = 2 * np.pi
d = len(x)
return -a * np.exp(-b * np.sqrt(sum(x ** 2) / d)) - np.exp(sum(np.cos(c * x)) / d) + a + np.exp(1)
```
7. Michalewicz Function(Michalewicz函数):
```python
import numpy as np
def michalewicz(x):
m = 10
return -sum(np.sin(x) * np.sin(np.arange(1, len(x) + 1) * x ** 2 / np.pi) ** (2 * m))
```
8. Levy Function(Levy函数):
```python
import numpy as np
def levy(x):
w = 1 + (x - 1) / 4.0
return (np.sin(np.pi * w[0]) ** 2 + sum((w[:-1] - 1) ** 2 * (1 + 10 * np.sin(np.pi * w[:-1] + 1) ** 2)) + (w[-1] - 1) ** 2 * (1 + np.sin(2 * np.pi * w[-1]) ** 2))
```
9. HappyCat Function(HappyCat函数):
```python
import numpy as np
def happycat(x):
alpha = 1.0 / 8
return (np.power(np.abs(sum(x ** 2) - len(x)), 2) ** alpha + (0.5 * sum(x ** 2) + sum(x)) / len(x) + 0.5)
```
10. Schwefel 2.22 Function(Schwefel 2.22函数):
```python
import numpy as np
def schwefel_222(x):
return max(abs(x))
```
以上是一些比较经典的测试函数,还有很多其他的测试函数可供使用。无论是哪一个测试函数,我们都可以通过Python代码实现其函数表达式。