模拟退火的算法Python

模拟退火算法是一种基于Monte-Carlo迭代求解策略的随机寻优算法。它主要用于解决组合优化问题。在Python中，可以使用以下代码实现模拟退火算法：

# 导入依赖库
import random
import math

# 定义目标函数
def objective_function(x):
    # 计算目标函数的值
    ...

# 定义模拟退火算法
def simulated_annealing():
    # 初始化当前解和当前目标函数值
    current_solution = ...
    current_objective = objective_function(current_solution)
    
    # 初始化最优解和最优目标函数值
    best_solution = current_solution
    best_objective = current_objective
    
    # 设定初始温度和终止温度
    initial_temperature = ...
    final_temperature = ...
    
    # 设定退火速率
    cooling_rate = ...
    
    # 迭代搜索
    while current_temperature > final_temperature:
        # 生成新解
        new_solution = ...
        
        # 计算新解的目标函数值
        new_objective = objective_function(new_solution)
        
        # 判断是否接受新解
        if new_objective < current_objective:
            current_solution = new_solution
            current_objective = new_objective
        else:
            probability = math.exp((current_objective - new_objective) / current_temperature)
            if random.random() < probability:
                current_solution = new_solution
                current_objective = new_objective
        
        # 更新最优解和最优目标函数值
        if current_objective < best_objective:
            best_solution = current_solution
            best_objective = current_objective
        
        # 降低温度
        current_temperature *= cooling_rate
    
    # 返回最优解和最优目标函数值
    return best_solution, best_objective

# 调用模拟退火算法
best_solution, best_objective = simulated_annealing()