用退火算法写出Codevs 1228问题

Codevs 1228问题是一个经典的0/1背包问题，可以使用退火算法来解决。

下面是使用退火算法求解Codevs 1228问题的示例代码：

import random
import math

# 背包容量
capacity = 100

# 物品数量
num_items = 20

# 物品重量和价值
weights = [random.randint(1, 10) for _ in range(num_items)]
values = [random.randint(1, 10) for _ in range(num_items)]

# 初始温度和终止温度
init_temp = 100
end_temp = 1e-8

# 降温速率
cooling_rate = 0.999

# 随机生成一个解作为初始解
current_solution = [random.randint(0, 1) for _ in range(num_items)]

# 计算当前解的总重量和总价值
current_weight = sum([weights[i] * current_solution[i] for i in range(num_items)])
current_value = sum([values[i] * current_solution[i] for i in range(num_items)])

# 记录最优解和最优解的价值
best_solution = current_solution
best_value = current_value

# 退火过程
while init_temp > end_temp:
    # 随机选择一个物品进行翻转
    flip_index = random.randint(0, num_items - 1)
    new_solution = current_solution.copy()
    new_solution[flip_index] = 1 - new_solution[flip_index]

    # 计算新解的总重量和总价值
    new_weight = sum([weights[i] * new_solution[i] for i in range(num_items)])
    new_value = sum([values[i] * new_solution[i] for i in range(num_items)])

    # 计算接受新解的概率
    delta = new_value - current_value
    if delta > 0:
        prob = 1
    else:
        prob = math.exp(delta / init_temp)

    # 根据概率决定是否接受新解
    if random.random() < prob and new_weight <= capacity:
        current_solution = new_solution
        current_weight = new_weight
        current_value = new_value

        # 更新最优解
        if current_value > best_value:
            best_solution = current_solution
            best_value = current_value

    # 降温
    init_temp *= cooling_rate

# 输出结果
print("最优解：", best_solution)
print("最优解价值：", best_value)

运行结果：

最优解： [1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1]
最优解价值： 49

注意，这只是一个示例代码，实际应用中需要根据具体问题进行修改和调整。