python模拟退火算法代码

当然，我可以为您提供一个简单的Python模拟退火算法的代码示例。以下是一个基本的实现：

import math
import random

def simulated_annealing(cost_func, initial_solution, initial_temperature, cooling_rate):
    current_solution = initial_solution
    best_solution = initial_solution

    current_temperature = initial_temperature

    while current_temperature > 0.01:
        neighbor = generate_neighbor(current_solution)
        current_cost = cost_func(current_solution)
        neighbor_cost = cost_func(neighbor)

        delta_cost = neighbor_cost - current_cost

        if delta_cost < 0:
            current_solution = neighbor
            if neighbor_cost < cost_func(best_solution):
                best_solution = neighbor
        elif random.random() < math.exp(-delta_cost / current_temperature):
            current_solution = neighbor

        current_temperature *= cooling_rate

    return best_solution

# 定义一个简单的代价函数（示例）
def cost_func(solution):
    # 假设我们要最小化的目标是一个简单的二维函数 f(x, y) = (x-3)^2 + (y-4)^2
    x, y = solution
    return (x - 3) ** 2 + (y - 4) ** 2

# 生成一个邻居解（示例）
def generate_neighbor(solution):
    x, y = solution
    return (x + random.uniform(-0.1, 0.1), y + random.uniform(-0.1, 0.1))

# 设置初始解、初始温度和冷却率
initial_solution = (0, 0)
initial_temperature = 100
cooling_rate = 0.99

# 运行模拟退火算法
best_solution = simulated_annealing(cost_func, initial_solution, initial_temperature, cooling_rate)

print("最优解：", best_solution)

请注意，以上代码只是模拟退火算法的一个基本实现，您可以根据自己的具体问题进行修改和扩展。