path: root/multiple_ant_colony_system.py

import numpy as np
import random
from vprtw_aco_figure import VrptwAcoFigure
from vrptw_base import VrptwGraph, PathMessage
from ant import Ant
from threading import Thread, Event
from queue import Queue
from concurrent.futures import ThreadPoolExecutor
import copy
import time
from multiprocessing import Process
from multiprocessing import Queue as MPQueue


class MultipleAntColonySystem:
    """
    Attributes
    ----------
    graph : VrptwGraph
    ants_num : int
    max_load : int
    beta : float
        Heuristic information importance (relative to pheromones)
    q0 : float
        Probability of directly selecting the next point with highest
        probability ??
    """
    def __init__(self, graph: VrptwGraph, ants_num=10, beta=1, q0=0.1, whether_or_not_to_show_figure=True):
        super()
        # The location and service time information of graph nodes
        self.graph = graph
        # ants_num number of ants
        self.ants_num = ants_num
        # vehicle_capacity represents the maximum load per vehicle
        self.max_load = graph.vehicle_capacity
        # beta heuristic information importance
        self.beta = beta
        # q0 represents the probability of directly selecting the next point
        # with the highest probability
        self.q0 = q0
        # best path
        self.best_path_distance = None
        self.best_path = None
        self.best_vehicle_num = None

        self.whether_or_not_to_show_figure = whether_or_not_to_show_figure

    @staticmethod
    def stochastic_accept(index_to_visit, transition_prob):
        """
        Roulette
        :param index_to_visit: a list of N index (list or tuple)
        :param transition_prob:
        :return: selected index
        """
        # calculate N and max fitness value
        N = len(index_to_visit)

        # normalize
        sum_tran_prob = np.sum(transition_prob)
        norm_transition_prob = transition_prob/sum_tran_prob

        # select: O(1)
        while True:
            # randomly select an individual with uniform probability
            ind = int(N * random.random())
            if random.random() <= norm_transition_prob[ind]:
                return index_to_visit[ind]

    @staticmethod
    def new_active_ant(ant: Ant, vehicle_num: int, local_search: bool, 
                       IN: np.numarray, q0: float, beta: int, stop_event: Event):
        """
        Explore the map according to the specified vehicle_num. The vehicle num
        used cannot be more than the specified number. This method is used by
        both acs_time and acs_vehicle

        For acs_time, it is necessary to visit all nodes (the path is
        feasible), and try to find a path with a shorter travel distance

        For acs_vehicle, the vehicle num used will be one less vehicle than the
        number of vehicles used by the currently found best path. To use fewer
        vehicles, try to visit the nodes. If all nodes are visited (the path is
        feasible), it will notify macs

        Parameters
        ----------
        ant : Ant
        vehicle_num : int
        local_search : bool
        IN : numpy.numarray
            ??? (variable compartida entre acs_vehicle y acs_time a traves de
            macs.  Pero que info tiene???
        q0 : float
        beta : int
        stop_event : threading.Event

        Returns
        -------
        """
        # print('[new_active_ant]: start, start_index %d' % ant.travel_path[0])

        # In new_active_ant, up to vehicle_num vehicles can be used, that is, it
        # can contain up to vehicle_num+1 depot nodes. Since one departure node
        # is used up, only vehicle depots are left.
        unused_depot_count = vehicle_num

        # If there are still unvisited nodes, and you can also return to the depot
        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

            # Calculate all next nodes that satisfy constraints such as load
            next_index_meet_constrains = ant.cal_next_index_meet_constrains()

            # If there is no next node that meets the limit, go back to the depot
            if len(next_index_meet_constrains) == 0:
                ant.move_to_next_index(0)
                unused_depot_count -= 1
                continue

            # Start calculating the next node that satisfies the constraints,
            # and select the probability of each node
            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)

            # Directly select the node with the largest closeness according to the probability
            if np.random.rand() < q0:
                max_prob_index = np.argmax(transition_prob)
                next_index = next_index_meet_constrains[max_prob_index]
            else:
                # Use the roulette algorithm
                next_index = MultipleAntColonySystem.stochastic_accept(next_index_meet_constrains, transition_prob)

            # update pheromone matrix
            ant.graph.local_update_pheromone(ant.current_index, next_index)
            ant.move_to_next_index(next_index)

        # If you finish all the points, you need to go back to the depot
        if ant.index_to_visit_empty():
            ant.graph.local_update_pheromone(ant.current_index, 0)
            ant.move_to_next_index(0)

        # Insert unvisited points to ensure that the path is feasible
        ant.insertion_procedure(stop_event)

        # ant.index_to_visit_empty()==True means Feasible
        if local_search is True and ant.index_to_visit_empty():
            ant.local_search_procedure(stop_event)

    @staticmethod
    def acs_time(new_graph: VrptwGraph, vehicle_num: int, ants_num: int, q0: float, beta: int,
                 global_path_queue: Queue, path_found_queue: Queue, stop_event: Event):
        """
        For acs_time, it is necessary to visit all nodes (the path is feasible),
        and try to find a path with a shorter travel distance
        :param new_graph:
        :param vehicle_num:
        :param ants_num:
        :param q0:
        :param beta:
        :param global_path_queue:
        :param path_found_queue:
        :param stop_event:
        :return:
        """

        # At most vehicle_num vehicles can be used, that is, the path contains
        # at most vehicle_num+1 depots to find the path with the shortest
        # distance, vehicle_num is set to be consistent with the current best_path
        print('[acs_time]: start, vehicle_num %d' % vehicle_num)
        # Initialize the pheromone matrix
        global_best_path = None
        global_best_distance = None
        ants_pool = ThreadPoolExecutor(ants_num)
        ants_thread = []
        ants = []
        while True:
            print('[acs_time]: new iteration')

            if stop_event.is_set():
                print('[acs_time]: receive stop event')
                return

            for k in range(ants_num):
                ant = Ant(new_graph, 0)
                thread = ants_pool.submit(MultipleAntColonySystem.new_active_ant, ant, vehicle_num, True,
                                          np.zeros(new_graph.node_num), q0, beta, stop_event)
                ants_thread.append(thread)
                ants.append(ant)

            # Here you can use the result method to wait for the thread to finish running
            for thread in ants_thread:
                thread.result()

            ant_best_travel_distance = None
            ant_best_path = None
            # Determine whether the path found by ants is feasible and better than the global path
            for ant in ants:

                if stop_event.is_set():
                    print('[acs_time]: receive stop event')
                    return

                # 获取当前的best path
                if not global_path_queue.empty():
                    info = global_path_queue.get()
                    while not global_path_queue.empty():
                        info = global_path_queue.get()
                    print('[acs_time]: receive global path info')
                    global_best_path, global_best_distance, global_used_vehicle_num = info.get_path_info()

                # 路径蚂蚁计算得到的最短路径
                if ant.index_to_visit_empty() and (ant_best_travel_distance is None or ant.total_travel_distance < ant_best_travel_distance):
                    ant_best_travel_distance = ant.total_travel_distance
                    ant_best_path = ant.travel_path

            # 在这里执行信息素的全局更新
            new_graph.global_update_pheromone(global_best_path, global_best_distance)

            # 向macs发送计算得到的当前的最佳路径
            if ant_best_travel_distance is not None and ant_best_travel_distance < global_best_distance:
                print('[acs_time]: ants\' local search found a improved feasible path, send path info to macs')
                path_found_queue.put(PathMessage(ant_best_path, ant_best_travel_distance))

            ants_thread.clear()
            for ant in ants:
                ant.clear()
                del ant
            ants.clear()

    @staticmethod
    def acs_vehicle(new_graph: VrptwGraph, vehicle_num: int, ants_num: int, q0: float, beta: int,
                    global_path_queue: Queue, path_found_queue: Queue, stop_event: Event):
        """
        For acs_vehicle, the vehicle num used will be one less vehicle than the
        number of vehicles used by the currently found best path. To use fewer
        vehicles, try to visit the nodes. If all nodes are visited (the path is
        feasible), it will notify macs

        Parameters
        ----------
        new_graph : VrptwGraph
        global_path_queue : queue.Queue
        path_found_queue : queue.Queue
        stop_event : threading.Event
        """
        # vehicle_num设置为比当前的best_path少一个
        print('[acs_vehicle]: start, vehicle_num %d' % vehicle_num)
        global_best_path = None
        global_best_distance = None

        # 使用nearest_neighbor_heuristic算法初始化path 和distance
        current_path, current_path_distance, _ = new_graph.nearest_neighbor_heuristic(max_vehicle_num=vehicle_num)

        # 找出当前path中未访问的结点
        current_index_to_visit = list(range(new_graph.node_num))
        for ind in set(current_path):
            current_index_to_visit.remove(ind)

        ants_pool = ThreadPoolExecutor(ants_num)
        ants_thread = []
        ants = []
        IN = np.zeros(new_graph.node_num)
        while True:
            print('[acs_vehicle]: new iteration')

            if stop_event.is_set():
                print('[acs_vehicle]: receive stop event')
                return

            for k in range(ants_num):
                ant = Ant(new_graph, 0)
                thread = ants_pool.submit(MultipleAntColonySystem.new_active_ant, 
                                          ant, vehicle_num, False, IN, q0,
                                          beta, stop_event)

                ants_thread.append(thread)
                ants.append(ant)

            # Here you can use the result method to wait for the thread to finish running
            for thread in ants_thread:
                thread.result()

            for ant in ants:

                if stop_event.is_set():
                    print('[acs_vehicle]: receive stop event')
                    return

                IN[ant.index_to_visit] = IN[ant.index_to_visit]+1

                # Compare the path found by the ants with the current_path,
                # whether it can use vehicle_num vehicles to visit more nodes
                if len(ant.index_to_visit) < len(current_index_to_visit):
                    current_path = copy.deepcopy(ant.travel_path)
                    current_index_to_visit = copy.deepcopy(ant.index_to_visit)
                    current_path_distance = ant.total_travel_distance
                    # and set IN to 0
                    IN = np.zeros(new_graph.node_num)

                    # If this path is feasible, it should be sent to macs_vrptw
                    if ant.index_to_visit_empty():
                        print('[acs_vehicle]: found a feasible path, send path info to macs')
                        path_found_queue.put(PathMessage(ant.travel_path, ant.total_travel_distance))

            # Update pheromone in new_graph, global
            new_graph.global_update_pheromone(current_path, current_path_distance)

            if not global_path_queue.empty():
                info = global_path_queue.get()
                while not global_path_queue.empty():
                    info = global_path_queue.get()
                print('[acs_vehicle]: receive global path info')
                global_best_path, global_best_distance, global_used_vehicle_num = info.get_path_info()

            new_graph.global_update_pheromone(global_best_path, global_best_distance)

            ants_thread.clear()
            for ant in ants:
                ant.clear()
                del ant
            ants.clear()

    def run_multiple_ant_colony_system(self, file_to_write_path=None):
        """
        Start another thread to run multiple_ant_colony_system, use the main thread for drawing
        """
        path_queue_for_figure = MPQueue()
        multiple_ant_colony_system_thread = Process(target=self._multiple_ant_colony_system, args=(path_queue_for_figure, file_to_write_path, ))
        multiple_ant_colony_system_thread.start()

        # Whether to show figure
        if self.whether_or_not_to_show_figure:
            figure = VrptwAcoFigure(self.graph.nodes, path_queue_for_figure)
            figure.run()
        multiple_ant_colony_system_thread.join()

    def _multiple_ant_colony_system(self, path_queue_for_figure: MPQueue, file_to_write_path=None):
        """
        Call acs_time and acs_vehicle for path exploration
        :param path_queue_for_figure:
        :return:
        """
        if file_to_write_path is not None:
            file_to_write = open(file_to_write_path, 'w')
        else:
            file_to_write = None

        start_time_total = time.time()

        # Two queues are needed here, time_what_to_do, vehicle_what_to_do, to
        # tell the two threads acs_time and acs_vehicle what the current best
        # path is, or let them stop computing
        global_path_to_acs_time = Queue()
        global_path_to_acs_vehicle = Queue()

        # Another queue, path_found_queue, is to receive acs_time and
        # acs_vehicle calculated by acs_vehicle that is even better than the
        # best path. Feasible path
        path_found_queue = Queue()

        # Initialize using the nearest neighbor algorithm
        self.best_path, self.best_path_distance, self.best_vehicle_num = self.graph.nearest_neighbor_heuristic()
        path_queue_for_figure.put(PathMessage(self.best_path, self.best_path_distance))

        while True:
            print('[multiple_ant_colony_system]: new iteration')
            start_time_found_improved_solution = time.time()

            # The information of the current best path is placed in the queue to
            # inform acs_time and acs_vehicle what the current best_path is
            global_path_to_acs_vehicle.put(PathMessage(self.best_path, self.best_path_distance))
            global_path_to_acs_time.put(PathMessage(self.best_path, self.best_path_distance))

            stop_event = Event()

            # acs_vehicle, try to explore with self.best_vehicle_num-1 vehicles, visit more nodes
            graph_for_acs_vehicle = self.graph.copy(self.graph.init_pheromone_val)
            acs_vehicle_thread = Thread(target=MultipleAntColonySystem.acs_vehicle,
                                        args=(graph_for_acs_vehicle, self.best_vehicle_num-1, self.ants_num, self.q0,
                                              self.beta, global_path_to_acs_vehicle, path_found_queue, stop_event))

            # acs_time try to explore with self.best_vehicle_num vehicles to find a shorter path
            graph_for_acs_time = self.graph.copy(self.graph.init_pheromone_val)
            acs_time_thread = Thread(target=MultipleAntColonySystem.acs_time,
                                     args=(graph_for_acs_time, self.best_vehicle_num, self.ants_num, self.q0, self.beta,
                                           global_path_to_acs_time, path_found_queue, stop_event))

            # Start acs_vehicle_thread and acs_time_thread, when they find a
            # feasible and better path than the best path, they will be sent to
            # macs
            print('[macs]: start acs_vehicle and acs_time')
            acs_vehicle_thread.start()
            acs_time_thread.start()

            best_vehicle_num = self.best_vehicle_num

            while acs_vehicle_thread.is_alive() and acs_time_thread.is_alive():

                # Exit the program if no better results are found within the specified time
                given_time = 10
                if time.time() - start_time_found_improved_solution > 60 * given_time:
                    stop_event.set()
                    self.print_and_write_in_file(file_to_write, '*' * 50)
                    self.print_and_write_in_file(file_to_write, 'time is up: cannot find a better solution in given time(%d minutes)' % given_time)
                    self.print_and_write_in_file(file_to_write, 'it takes %0.3f second from multiple_ant_colony_system running' % (time.time()-start_time_total))
                    self.print_and_write_in_file(file_to_write, 'the best path have found is:')
                    self.print_and_write_in_file(file_to_write, self.best_path)
                    self.print_and_write_in_file(file_to_write, 'best path distance is %f, best vehicle_num is %d' % (self.best_path_distance, self.best_vehicle_num))
                    self.print_and_write_in_file(file_to_write, '*' * 50)

                    # Pass in None as the end flag
                    if self.whether_or_not_to_show_figure:
                        path_queue_for_figure.put(PathMessage(None, None))

                    if file_to_write is not None:
                        file_to_write.flush()
                        file_to_write.close()
                    return

                if path_found_queue.empty():
                    continue

                path_info = path_found_queue.get()
                print('[macs]: receive found path info')
                found_path, found_path_distance, found_path_used_vehicle_num = path_info.get_path_info()
                while not path_found_queue.empty():
                    path, distance, vehicle_num = path_found_queue.get().get_path_info()

                    if distance < found_path_distance:
                        found_path, found_path_distance, found_path_used_vehicle_num = path, distance, vehicle_num

                    if vehicle_num < found_path_used_vehicle_num:
                        found_path, found_path_distance, found_path_used_vehicle_num = path, distance, vehicle_num

                # If the distance of the found path (which is feasible) is
                # shorter, update the current best path information
                if found_path_distance < self.best_path_distance:

                    # Better search results, update start_time
                    start_time_found_improved_solution = time.time()

                    self.print_and_write_in_file(file_to_write, '*' * 50)
                    self.print_and_write_in_file(file_to_write, '[macs]: distance of found path (%f) better than best path\'s (%f)' % (found_path_distance, self.best_path_distance))
                    self.print_and_write_in_file(file_to_write, 'it takes %0.3f second from multiple_ant_colony_system running' % (time.time()-start_time_total))
                    self.print_and_write_in_file(file_to_write, '*' * 50)
                    if file_to_write is not None:
                        file_to_write.flush()

                    self.best_path = found_path
                    self.best_vehicle_num = found_path_used_vehicle_num
                    self.best_path_distance = found_path_distance

                    # If you need to draw graphics, send the best path to be found to the drawing program
                    if self.whether_or_not_to_show_figure:
                        path_queue_for_figure.put(PathMessage(self.best_path, self.best_path_distance))

                    # Notify the two threads of acs_vehicle and acs_time, the
                    # currently found best_path and best_path_distance
                    global_path_to_acs_vehicle.put(PathMessage(self.best_path, self.best_path_distance))
                    global_path_to_acs_time.put(PathMessage(self.best_path, self.best_path_distance))

                # If the paths found by the two threads use fewer vehicles, stop
                # the two threads and start the next iteration
                # Send stop messages to acs_time and acs_vehicle
                if found_path_used_vehicle_num < best_vehicle_num:

                    # Better search results, update start_time
                    start_time_found_improved_solution = time.time()
                    self.print_and_write_in_file(file_to_write, '*' * 50)
                    self.print_and_write_in_file(file_to_write, '[macs]: vehicle num of found path (%d) better than best path\'s (%d), found path distance is %f'
                          % (found_path_used_vehicle_num, best_vehicle_num, found_path_distance))
                    self.print_and_write_in_file(file_to_write, 'it takes %0.3f second multiple_ant_colony_system running' % (time.time() - start_time_total))
                    self.print_and_write_in_file(file_to_write, '*' * 50)
                    if file_to_write is not None:
                        file_to_write.flush()

                    self.best_path = found_path
                    self.best_vehicle_num = found_path_used_vehicle_num
                    self.best_path_distance = found_path_distance

                    if self.whether_or_not_to_show_figure:
                        path_queue_for_figure.put(PathMessage(self.best_path, self.best_path_distance))

                    # Stop the acs_time and acs_vehicle threads
                    print('[macs]: send stop info to acs_time and acs_vehicle')
                    # Notify the two threads of acs_vehicle and acs_time, the
                    # currently found best_path and best_path_distance
                    stop_event.set()

    @staticmethod
    def print_and_write_in_file(file_to_write=None, message='default message'):
        if file_to_write is None:
            print(message)
        else:
            print(message)
            file_to_write.write(str(message)+'\n')