模拟退火算法
模拟退火算法（Simulated Annealing）是一种启发式优化算法，常用于解决组合优化问题。
它通过模拟固体退火过程中的温度变化来逐步降低系统能量，以在搜索空间中寻找全局最优
解或近似最优解。
下面是一个简单的模拟退火算法的伪代码实现：
```python
function simulated_annealing(problem, initial_solution, initial_temperature, cooling_rate,
stopping_temperature):
current_solution = initial_solution
current_energy = problem.evaluate(current_solution)
temperature = initial_temperature
while temperature > stopping_temperature:
# Generate a new solution by making a small change to the current solution
new_solution = problem.generate_neighbor(current_solution)
# Calculate the energy of the new solution
new_energy = problem.evaluate(new_solution)
# Calculate the energy difference between the new and current solutions
energy_difference = new_energy - current_energy
# If the new solution is better, accept it
if energy_difference < 0:
current_solution = new_solution
current_energy = new_energy
else:
# If the new solution is worse, accept it with a probability determined by the
temperature
if random_number() < exp(-energy_difference / temperature):
current_solution = new_solution
current_energy = new_energy
# Reduce the temperature according to the cooling rate
temperature *= cooling_rate
return current_solution, current_energy
```
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