模拟退火模型python代码

模拟退火是一种优化算法，用于寻找全局最优解。在Python中实现模拟退火通常涉及几个步骤，包括初始化、评估当前状态、接受新状态的概率计算以及循环更新等。以下是一个简单的模拟退火算法的Python示例，用于在一个二维数组中找到最小值：

import random

def acceptance_probability(current_val, new_val, temp):
    return min(1, exp((new_val - current_val) / temp))

def random_neighbor(grid, row, col):
    # 生成随机邻居的行和列
    neighbor_row = (row + random.randint(-1, 1)) % len(grid)
    neighbor_col = (col + random.randint(-1, 1)) % len(grid[0])
    return grid[neighbor_row][neighbor_col]

def simulated_annealing(grid, initial_temp, cooling_rate, max_steps):
    row, col = random.choice(list(range(len(grid))))
    current_val = grid[row][col]
    best_val = current_val
    best_pos = [row, col]

    for step in range(max_steps):
        new_val = grid[random_neighbor(grid, *best_pos)]  # 选择随机邻居
        if new_val < current_val or random.random() < acceptance_probability(current_val, new_val, initial_temp):
            current_val = new_val
            best_pos = [random_neighbor.grid, *best_pos]  # 更新最佳位置
        if current_val == min(grid.flat):  # 如果达到全局最小值，提前结束
            break
        initial_temp *= cooling_rate  # 冷却过程

    return best_pos, current_val

# 示例网格
grid = [[5, 4, 6], [3, 8, 7], [9, 2, 1]]
initial_temp = 100
cooling_rate = 0.99
max_steps = 1000

result, _ = simulated_annealing(grid, initial_temp, cooling_rate, max_steps)
print(f"全局最小值在 ({result[0]}, {result[1]})，值为: {grid[result[0]][result[1]]}")