用Python写一个模拟退火算法代码
时间: 2024-03-28 17:14:05 浏览: 9
以下是一个简单的模拟退火算法的 Python 代码示例:
```
import random
import math
def simulated_annealing(cost_function, initial_solution, temperature, cooling_rate, stopping_temperature):
current_solution = initial_solution
current_cost = cost_function(current_solution)
best_solution = current_solution
best_cost = current_cost
while temperature > stopping_temperature:
neighbor_solution = get_neighbor(current_solution)
neighbor_cost = cost_function(neighbor_solution)
delta = neighbor_cost - current_cost
if delta < 0 or math.exp(-delta / temperature) > random.random():
current_solution = neighbor_solution
current_cost = neighbor_cost
if current_cost < best_cost:
best_solution = current_solution
best_cost = current_cost
temperature *= cooling_rate
return best_solution, best_cost
def get_neighbor(solution):
# 生成一个邻居解
return ...
def cost_function(solution):
# 计算解的成本
return ...
# 示例使用
initial_solution = ...
temperature = 1000
cooling_rate = 0.95
stopping_temperature = 1e-8
best_solution, best_cost = simulated_annealing(cost_function, initial_solution, temperature, cooling_rate, stopping_temperature)
print("Best solution:", best_solution)
print("Best cost:", best_cost)
```
在这个例子中,`simulated_annealing` 函数是模拟退火算法的主要部分。它接受一个成本函数、一个初始解、一个初始温度、一个冷却率和一个停止温度。它使用随机邻居解和概率函数来决定是否接受新的解,从而在搜索空间中寻找最优解。`get_neighbor` 函数生成一个随机邻居解,而 `cost_function` 函数计算一个解的成本。最后,示例代码演示了如何使用 `simulated_annealing` 函数来解决一个问题并输出最优解和成本。