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 class MultipleAntColonySystem: def __init__(self, graph: VrptwGraph, ants_num=10, beta=2, q0=0.1, whether_or_not_to_show_figure=True): super() # graph 结点的位置、服务时间信息 self.graph = graph # ants_num 蚂蚁数量 self.ants_num = ants_num # vehicle_capacity 表示每辆车的最大载重 self.max_load = graph.vehicle_capacity # beta 启发性信息重要性 self.beta = beta # q0 表示直接选择概率最大的下一点的概率 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): """ 轮盘赌 :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): """ 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.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) @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): """ 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 :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: """ # 最多可以使用vehicle_num辆车，即在path中最多包含vehicle_num+1个depot中，找到路程最短的路径， # vehicle_num设置为与当前的best_path一致 print('[acs_time]: start, vehicle_num %d' % vehicle_num) # 初始化信息素矩阵 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) # 这里可以使用result方法，等待线程跑完 for thread in ants_thread: thread.result() # 判断蚂蚁找出来的路径是否是feasible的，并且比全局的路径要好 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() # 如果比全局的路径要好，则要将该路径发送到macs中 if ant.index_to_visit_empty() and ant.total_travel_distance < global_best_distance: print('[acs_time]: ant found a improved feasible path, send path info to macs') path_found_queue.put(PathMessage(ant.travel_path, ant.total_travel_distance)) # 在这里执行信息素的全局更新 new_graph.global_update_pheromone(global_best_path, global_best_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): """ 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 new_graph: :param vehicle_num: :param ants_num: :param q0: :param beta: :param global_path_queue: :param path_found_queue: :param stop_event: :return: """ # 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() # 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) # 这里可以使用result方法，等待线程跑完 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 # 蚂蚁找出来的路径与current_path进行比较，是否能使用vehicle_num辆车访问到更多的结点 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 # 并且将IN设置为0 IN = np.zeros(new_graph.node_num) # 如果这一条路径是feasible的话，就要发到macs_vrptw中 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)) # 更新new_graph中的信息素，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): """ 开启另外的线程来跑multiple_ant_colony_system， 使用主线程来绘图 :return: """ path_queue_for_figure = Queue() multiple_ant_colony_system_thread = Thread(target=self._multiple_ant_colony_system, args=(path_queue_for_figure,)) multiple_ant_colony_system_thread.start() # 是否要展示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() # 传入None作为结束标志 if self.whether_or_not_to_show_figure: path_queue_for_figure.put(PathMessage(None, None)) def _multiple_ant_colony_system(self, path_queue_for_figure: Queue): """ 调用acs_time 和 acs_vehicle进行路径的探索 :param path_queue_for_figure: :return: """ start_time_total = time.time() # 在这里需要两个队列，time_what_to_do、vehicle_what_to_do， 用来告诉acs_time、acs_vehicle这两个线程，当前的best path是什么，或者让他们停止计算 global_path_to_acs_time = Queue() global_path_to_acs_vehicle = Queue() # 另外的一个队列， path_found_queue就是接收acs_time 和acs_vehicle计算出来的比best path还要好的feasible path path_found_queue = Queue() # 使用近邻点算法初始化 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() # 当前best path的信息，放在queue中以通知acs_time和acs_vehicle当前的best_path是什么 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，尝试以self.best_vehicle_num-1辆车去探索，访问更多的结点 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 尝试以self.best_vehicle_num辆车去探索，找到更短的路径 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)) # 启动acs_vehicle_thread和acs_time_thread，当他们找到feasible、且是比best path好的路径时，就会发送到macs中来 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(): # 如果在指定时间内没有搜索到更好的结果，则退出程序 if time.time() - start_time_found_improved_solution > 60 * 5: stop_event.set() print('*' * 50) print('time is up: cannot find a better solution in given time') print('it takes %0.3f second from multiple_ant_colony_system running' % (time.time()-start_time_total)) print('*' * 50) 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 # 如果找到的路径（which is feasible）的距离更短，则更新当前的最佳path的信息 if found_path_distance < self.best_path_distance: # 搜索到更好的结果，更新start_time start_time_found_improved_solution = time.time() print('*' * 50) print('[macs]: distance of found path (%f) better than best path\'s (%f)' % (found_path_distance, self.best_path_distance)) print('it takes %0.3f second from multiple_ant_colony_system running' % (time.time()-start_time_total)) print('*' * 50) self.best_path = found_path self.best_vehicle_num = found_path_used_vehicle_num self.best_path_distance = found_path_distance # 如果需要绘制图形，则要找到的best path发送给绘图程序 if self.whether_or_not_to_show_figure: path_queue_for_figure.put(PathMessage(self.best_path, self.best_path_distance)) # 通知acs_vehicle和acs_time两个线程，当前找到的best_path和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)) # 如果，这两个线程找到的路径用的车辆更少了，就停止这两个线程，开始下一轮迭代 # 向acs_time和acs_vehicle中发送停止信息 if found_path_used_vehicle_num < best_vehicle_num: # 搜索到更好的结果，更新start_time start_time_found_improved_solution = time.time() print('*' * 50) print('[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)) print('it takes %0.3f second multiple_ant_colony_system running' % (time.time() - start_time_total)) print('*' * 50) 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)) # 停止acs_time 和 acs_vehicle 两个线程 print('[macs]: send stop info to acs_time and acs_vehicle') # 通知acs_vehicle和acs_time两个线程，当前找到的best_path和best_path_distance stop_event.set()