禁忌搜索算法背包问题python

禁忌搜索算法是一种启发式搜索算法，用于解决组合优化问题，例如背包问题。下面是一个使用禁忌搜索算法解决背包问题的Python实现：

import random

def knapsack(items, capacity, tabu_size, max_iter):
    # 初始化禁忌表和当前解
    tabu_list = []
    current_solution = [0] * len(items)
    current_weight = 0
    current_value = 0
    best_solution = current_solution
    best_value = 0

    # 迭代
    for i in range(max_iter):
        # 生成邻域解
        neighbors = []
        for j in range(len(items)):
            neighbor = current_solution.copy()
            neighbor[j] = 1 - neighbor[j]
            if neighbor not in tabu_list:
                neighbors.append(neighbor)

        # 选择最优邻域解
        best_neighbor = None
        best_neighbor_value = 0
        for neighbor in neighbors:
            neighbor_weight = sum([item[0] for item in items if neighbor[items.index(item)]])
            if neighbor_weight <= capacity:
                neighbor_value = sum([item[1] for item in items if neighbor[items.index(item)]])
                if neighbor_value > best_neighbor_value:
                    best_neighbor = neighbor
                    best_neighbor_value = neighbor_value

        # 更新当前解和最优解
        if best_neighbor:
            current_solution = best_neighbor
            current_weight = sum([item[0] for item in items if current_solution[items.index(item)]])
            current_value = sum([item[1] for item in items if current_solution[items.index(item)]])
            if current_value > best_value:
                best_solution = current_solution
                best_value = current_value

        # 更新禁忌表
        if len(tabu_list) >= tabu_size:
            tabu_list.pop(0)
        tabu_list.append(current_solution)

    return best_solution, best_value

该函数接受三个参数：物品列表，背包容量，禁忌表大小和最大迭代次数。其中，物品列表包含每个物品的重量和价值，如[(4, 8), (3, 6), (1, 2), (5, 10)]表示有4个物品，它们的重量和价值分别为(4, 8), (3, 6), (1, 2), (5, 10)。禁忌表用于记录已经搜索过的解，避免重复搜索。在每次迭代中，算法会生成邻域解并选择最优邻域解更新当前解和最优解。如果当前解比最优解更好，则将当前解作为新的最优解。如果当前解是一个禁忌解，则不会更新禁忌表，否则将当前解添加到禁忌表中。最终，函数返回最优解和最优解的价值。