模拟退火算法python代码库

模拟退火算法是一种全局优化算法，用于在搜索空间中寻找最优解。下面是一个使用Python实现模拟退火算法的代码库示例：

import math
import random

def simulated_annealing(initial_state, cost_function, temperature, cooling_rate, stopping_temperature):
    current_state = initial_state
    current_cost = cost_function(current_state)
    best_state = current_state
    best_cost = current_cost

    while temperature > stopping_temperature:
        new_state = generate_neighbor(current_state)
        new_cost = cost_function(new_state)
        delta_cost = new_cost - current_cost

        if delta_cost < 0 or random.random() < math.exp(-delta_cost / temperature):
            current_state = new_state
            current_cost = new_cost

        if current_cost < best_cost:
            best_state = current_state
            best_cost = current_cost

        temperature *= cooling_rate

    return best_state, best_cost

def generate_neighbor(state):
    # 生成邻居状态的函数
    # 在这里实现生成邻居状态的逻辑
    pass

def cost_function(state):
    # 计算状态的成本函数
    # 在这里实现计算成本函数的逻辑
    pass

# 使用示例
initial_state = # 设置初始状态
temperature = # 设置初始温度
cooling_rate = # 设置冷却速率
stopping_temperature = # 设置停止温度

best_state, best_cost = simulated_annealing(initial_state, cost_function, temperature, cooling_rate, stopping_temperature)
print("Best state:", best_state)
print("Best cost:", best_cost)

你可以根据你的具体问题来实现`generate_neighbor`和`cost_function`函数。`generate_neighbor`函数用于生成邻居状态，而`cost_function`函数用于计算状态的成本。在使用时，你需要根据你的问题设置初始状态、初始温度、冷却速率和停止温度。