请给我一段变领域搜索算法解决PDP问题的python代码

好的，以下是一段使用变领域搜索算法解决PDP问题的Python代码，其中使用了基于禁忌搜索的变领域搜索算法（Tabu Search）：

import numpy as np

class PDP:
    def __init__(self, capacity, demand, distance, num_vehicles):
        self.capacity = capacity
        self.demand = demand
        self.distance = distance
        self.num_vehicles = num_vehicles
        self.num_nodes = len(demand)
        self.best_sol = None

    def generate_initial_solution(self):
        # 随机生成初始解
        sol = np.zeros((self.num_nodes, self.num_vehicles))
        for i in range(self.num_nodes):
            j = np.random.randint(self.num_vehicles)
            sol[i, j] = 1
        return sol

    def calc_obj(self, sol):
        # 计算解的目标函数值
        obj = 0
        for j in range(self.num_vehicles):
            load = 0
            dist = 0
            for i in range(self.num_nodes):
                if sol[i, j] == 1:
                    load += self.demand[i]
                    dist += self.distance[i][0]
                elif sol[i, j] == 2:
                    load -= self.demand[i]
                    dist += self.distance[0][i]
                if load > self.capacity:
                    return np.inf
            dist += self.distance[0][j+1]
            obj += dist
        return obj

    def shaking_one(self, sol):
        # 变邻域操作1
        route_list = []
        for j in range(self.num_vehicles):
            idx = np.where(sol[:, j] == 1)[0]
            if len(idx) > 0:
                i = np.random.choice(idx)
                k = np.random.randint(self.num_vehicles)
                while k == j:
                    k = np.random.randint(self.num_vehicles)
                sol[i, j] = 0
                sol[i, k] = 1
                route_list.append(((i, j), (i, k)))
        return route_list

    def shaking_two(self, sol):
        # 变邻域操作2
        route_list = []
        for j in range(self.num_vehicles):
            for i1 in range(self.num_nodes):
                if sol[i1, j] == 1:
                    for i2 in range(self.num_nodes):
                        if sol[i2, j] == 0 and i1 != i2:
                            sol[i1, j] = 0
                            sol[i2, j] = 1
                            if self.calc_obj(sol) < np.inf:
                                route_list.append(((i1, j), (i2, j)))
                                return route_list
                            sol[i1, j] = 1
                            sol[i2, j] = 0
        return route_list

    def tabu_search(self, max_iter, tabu_len):
        # 变领域搜索算法（基于禁忌搜索）
        sol = self.generate_initial_solution()
        best_sol = sol.copy()
        tabu_list = []
        tabu_iter = np.zeros((self.num_nodes, self.num_vehicles))
        iter_cnt = 0
        while iter_cnt < max_iter:
            iter_cnt += 1
            route_list1 = self.shaking_one(sol)
            route_list2 = self.shaking_two(sol)
            if len(route_list1) > 0 or len(route_list2) > 0:
                best_obj = np.inf
                best_route = None
                for route in route_list1 + route_list2:
                    i1, j1 = route[0]
                    i2, j2 = route[1]
                    if tabu_iter[i1, j1] > iter_cnt or tabu_iter[i2, j2] > iter_cnt:
                        continue
                    sol[i1, j1] = 0
                    sol[i2, j2] = 1
                    obj = self.calc_obj(sol)
                    if obj < best_obj:
                        best_obj = obj
                        best_sol = sol.copy()
                        best_route = route
                if best_route is not None:
                    i1, j1 = best_route[0]
                    i2, j2 = best_route[1]
                    sol[i1, j1] = 0
                    sol[i2, j2] = 1
                    tabu_list.append(best_route)
                    tabu_iter[i1, j1] = iter_cnt + tabu_len
                    tabu_iter[i2, j2] = iter_cnt + tabu_len
            else:
                break
        self.best_sol = best_sol
        return best_sol

上述代码中，PDP类封装了PDP问题的求解过程，包括随机生成初始解、计算目标函数值、变领域操作等。tabu_search方法实现了基于禁忌搜索的变领域搜索算法，其中max_iter参数表示最大迭代次数，tabu_len参数表示禁忌长度。在每一次迭代中，算法先进行变领域操作，然后从所有可行的邻域解中选择一个最优解，并更新当前解。如果找不到可行的邻域解，则算法终止。在搜索过程中，算法使用禁忌列表保存已经搜索过的解，以避免陷入局部最优解。