数学建模 模拟退火算法Python代码

数学建模是将实际问题抽象为数学模型，并通过数学方法进行求解和分析的过程。它在实际问题的解决中起到了重要的作用。而模拟退火算法是一种全局优化算法，常用于求解复杂的优化问题。下面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_func, initial_solution, initial_temperature, cooling_rate, max_iterations):
    current_solution = initial_solution
    best_solution = current_solution
    current_temperature = initial_temperature

    for i in range(max_iterations):
        new_solution = generate_neighbor(current_solution)
        current_cost = cost_func(current_solution)
        new_cost = cost_func(new_solution)

        if new_cost < current_cost:
            current_solution = new_solution
            if new_cost < cost_func(best_solution):
                best_solution = new_solution
        else:
            probability = math.exp((current_cost - new_cost) / current_temperature)
            if random.random() < probability:
                current_solution = new_solution

        current_temperature *= cooling_rate

    return best_solution

def generate_neighbor(solution):
    # 生成邻居解的方法，根据具体问题进行定义
    pass

def cost_func(solution):
    # 计算解的成本函数，根据具体问题进行定义
    pass

# 使用示例
initial_solution = [1, 2, 3, 4, 5]
initial_temperature = 100
cooling_rate = 0.95
max_iterations = 1000

best_solution = simulated_annealing(cost_func, initial_solution, initial_temperature, cooling_rate, max_iterations)
print("Best solution:", best_solution)

请注意，上述代码中的`generate_neighbor`函数和`cost_func`函数需要根据具体问题进行定义。`generate_neighbor`函数用于生成邻居解，而`cost_func`函数用于计算解的成本函数。在实际应用中，你需要根据具体的优化问题来编写这两个函数。