模拟退火算法的源代码

以下是一个简单的模拟退火算法的Python源代码示例：

import math
import random

def simulated_annealing(cost_func, initial_solution, initial_temperature, cooling_rate, stopping_temperature):
    current_solution = initial_solution
    best_solution = current_solution
    current_cost = cost_func(current_solution)
    best_cost = current_cost
    temperature = initial_temperature
    
    while temperature > stopping_temperature:
        neighbor_solution = generate_neighbor(current_solution)
        neighbor_cost = cost_func(neighbor_solution)
        
        if neighbor_cost < current_cost:
            current_solution = neighbor_solution
            current_cost = neighbor_cost
        else:
            accept_probability = math.exp((current_cost - neighbor_cost) / temperature)
            if random.random() < accept_probability:
                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 generate_neighbor(solution):
    # 根据当前解生成一个随机的邻居解
    # 这里省略具体实现
    pass

def cost_func(solution):
    # 计算当前解的成本
    # 这里省略具体实现
    pass

# 使用示例
initial_solution = [1, 2, 3, 4, 5]
initial_temperature = 1000
cooling_rate = 0.95
stopping_temperature = 0.1

best_solution, best_cost = simulated_annealing(cost_func, initial_solution, initial_temperature, cooling_rate, stopping_temperature)

print("Best solution:", best_solution)
print("Best cost:", best_cost)

请注意，上述代码是一个基本的模拟退火算法的框架，需要根据具体问题进行适当的修改和扩展。在实际应用中，需要根据问题的特点来定义邻域生成函数 generate_neighbor 和成本函数 cost_func。