模拟退火算法是一种基于随机搜索的优化算法,常用于解决函数优化问题。下面是一个简单的模拟退火算法的例程:
```python
import random
import math
# 目标函数
def objective_function(x, y):
return math.sin(x) + math.cos(y)
# 初始解
def initial_solution():
x = random.uniform(-10, 10)
y = random.uniform(-10, 10)
return x, y
# 邻域搜索
def neighbor_solution(x, y, step_size):
new_x = x + random.uniform(-step_size, step_size)
new_y = y + random.uniform(-step_size, step_size)
return new_x, new_y
# 接受更新的概率
def acceptance_probability(old_value, new_value, temperature):
if new_value > old_value:
return 1
else:
return math.exp((new_value - old_value) / temperature)
# 模拟退火算法
def simulated_annealing(max_iterations, initial_temperature, cooling_rate):
&nbs