Example #1
0
    def new_active_ant(ant: Ant, vehicle_num: int, local_search: bool,
                       IN: np.numarray, q0: float, beta: int,
                       stop_event: Event):
        """
        :param ant:
        :param vehicle_num:
        :param local_search:
        :param IN:
        :param q0:
        :param beta:
        :param stop_event:
        :return:
        """
        # print('[new_active_ant]: start, start_index %d' % ant.travel_path[0])

        unused_depot_count = vehicle_num

        while not ant.index_to_visit_empty() and unused_depot_count > 0:
            if stop_event.is_set():
                # print('[new_active_ant]: receive stop event')
                return

            next_index_meet_constrains = ant.cal_next_index_meet_constrains()

            if len(next_index_meet_constrains) == 0:
                ant.move_to_next_index(0)
                unused_depot_count -= 1
                continue

            length = len(next_index_meet_constrains)
            ready_time = np.zeros(length)
            due_time = np.zeros(length)

            for i in range(length):
                ready_time[i] = ant.graph.nodes[
                    next_index_meet_constrains[i]].ready_time
                due_time[i] = ant.graph.nodes[
                    next_index_meet_constrains[i]].due_time

            delivery_time = np.maximum(
                ant.vehicle_travel_time + ant.graph.node_dist_mat[
                    ant.current_index][next_index_meet_constrains], ready_time)
            delta_time = delivery_time - ant.vehicle_travel_time
            distance = delta_time * (due_time - ant.vehicle_travel_time)

            distance = np.maximum(1.0,
                                  distance - IN[next_index_meet_constrains])
            closeness = 1 / distance

            transition_prob = ant.graph.pheromone_mat[ant.current_index][next_index_meet_constrains] * \
                              np.power(closeness, beta)
            transition_prob = transition_prob / np.sum(transition_prob)

            if np.random.rand() < q0:
                max_prob_index = np.argmax(transition_prob)
                next_index = next_index_meet_constrains[max_prob_index]
            else:
                next_index = MultipleAntColonySystem.stochastic_accept(
                    next_index_meet_constrains, transition_prob)

            ant.graph.local_update_pheromone(ant.current_index, next_index)
            ant.move_to_next_index(next_index)

        if ant.index_to_visit_empty():
            ant.graph.local_update_pheromone(ant.current_index, 0)
            ant.move_to_next_index(0)

        ant.insertion_procedure(stop_event)

        if local_search is True and ant.index_to_visit_empty():
            ant.local_search_procedure(stop_event)
Example #2
0
    def new_active_ant(ant: Ant, vehicle_num: int, local_search: bool, IN: np.numarray, q0: float, beta: int, stop_event: Event):
        """
        按照指定的vehicle_num在地图上进行探索,所使用的vehicle num不能多于指定的数量,acs_time和acs_vehicle都会使用到这个方法
        对于acs_time来说,需要访问完所有的结点(路径是可行的),尽量找到travel distance更短的路径
        对于acs_vehicle来说,所使用的vehicle num会比当前所找到的best path所使用的车辆数少一辆,要使用更少的车辆,尽量去访问结点,如果访问完了所有的结点(路径是可行的),就将通知macs
        :param ant:
        :param vehicle_num:
        :param local_search:
        :param IN:
        :param q0:
        :param beta:
        :param stop_event:
        :return:
        """
        # print('[new_active_ant]: start, start_index %d' % ant.travel_path[0])

        # 在new_active_ant中,最多可以使用vehicle_num个车,即最多可以包含vehicle_num+1个depot结点,由于出发结点用掉了一个,所以只剩下vehicle个depot
        unused_depot_count = vehicle_num

        # 如果还有未访问的结点,并且还可以回到depot中
        while not ant.index_to_visit_empty() and unused_depot_count > 0:
            if stop_event.is_set():
                # print('[new_active_ant]: receive stop event')
                return

            # 计算所有满足载重等限制的下一个结点
            next_index_meet_constrains = ant.cal_next_index_meet_constrains()

            # 如果没有满足限制的下一个结点,则回到depot中
            if len(next_index_meet_constrains) == 0:
                ant.move_to_next_index(0)
                unused_depot_count -= 1
                continue

            # 开始计算满足限制的下一个结点,选择各个结点的概率
            length = len(next_index_meet_constrains)
            ready_time = np.zeros(length)
            due_time = np.zeros(length)

            for i in range(length):
                ready_time[i] = ant.graph.nodes[next_index_meet_constrains[i]].ready_time
                due_time[i] = ant.graph.nodes[next_index_meet_constrains[i]].due_time

            delivery_time = np.maximum(ant.vehicle_travel_time + ant.graph.node_dist_mat[ant.current_index][next_index_meet_constrains], ready_time)
            delta_time = delivery_time - ant.vehicle_travel_time
            distance = delta_time * (due_time - ant.vehicle_travel_time)

            distance = np.maximum(1.0, distance-IN[next_index_meet_constrains])
            closeness = 1/distance

            transition_prob = ant.graph.pheromone_mat[ant.current_index][next_index_meet_constrains] * \
                              np.power(closeness, beta)
            transition_prob = transition_prob / np.sum(transition_prob)

            # 按照概率直接选择closeness最大的结点
            if np.random.rand() < q0:
                max_prob_index = np.argmax(transition_prob)
                next_index = next_index_meet_constrains[max_prob_index]
            else:
                # 使用轮盘赌算法
                next_index = MultipleAntColonySystem.stochastic_accept(next_index_meet_constrains, transition_prob)

            # 更新信息素矩阵
            ant.graph.local_update_pheromone(ant.current_index, next_index)
            ant.move_to_next_index(next_index)

        # 如果走完所有的点了,需要回到depot
        if ant.index_to_visit_empty():
            ant.graph.local_update_pheromone(ant.current_index, 0)
            ant.move_to_next_index(0)

        # 对未访问的点进行插入,保证path是可行的
        ant.insertion_procedure(stop_event)

        # ant.index_to_visit_empty()==True就是feasible的意思
        if local_search is True and ant.index_to_visit_empty():
            ant.local_search_procedure(stop_event)