diff options
Diffstat (limited to 'multiple_ant_colony_system.py')
| -rw-r--r-- | multiple_ant_colony_system.py | 99 |
1 files changed, 22 insertions, 77 deletions
diff --git a/multiple_ant_colony_system.py b/multiple_ant_colony_system.py index 21e2fae..ea500b5 100644 --- a/multiple_ant_colony_system.py +++ b/multiple_ant_colony_system.py @@ -53,66 +53,6 @@ class MultipleAntColonySystem: return index_to_visit[ind] @staticmethod - def local_search_procedure_for_global_path(graph: VrptwGraph, global_best_path: list, global_best_distance: float, stop_event: Event): - """ - 对global_path使用cross进行局部搜索 - :return: - """ - result_path = None - result_distance = None - - new_path_travel_distance = [] - new_path = [] - for i in range(1, len(global_best_path)): - - if graph.nodes[global_best_path[i]].is_depot: - continue - - for j in range(i+1, len(global_best_path)): - - if graph.nodes[global_best_path[j]].is_depot: - continue - - if stop_event.is_set(): - # print('[local_search_procedure_for_global_path]: receive stop event') - return result_path, result_distance - - path = copy.deepcopy(global_best_path) - path[i], path[j] = path[j], path[i] - - # 判断新生成的path是否是可行的 - check_ant = Ant(graph, path[0]) - for ind in path[1:]: - if check_ant.check_condition(ind): - check_ant.move_to_next_index(ind) - else: - break - - # 如果新生成的path是可行的 - if check_ant.index_to_visit_empty() and check_ant.total_travel_distance < global_best_distance: - # print('success to search') - new_path_travel_distance.append(check_ant.total_travel_distance) - new_path.append(path) - else: - path.clear() - - # 找出新生成的path中，路程最小的 - if len(new_path_travel_distance) > 0: - new_path_travel_distance = np.array(new_path_travel_distance) - min_distance_ind = np.argmin(new_path_travel_distance) - - result_path = copy.deepcopy(new_path[int(min_distance_ind)]) - result_distance = new_path_travel_distance[int(min_distance_ind)] - - for i in range(len(new_path)): - new_path[i].clear() - new_path.clear() - del new_path_travel_distance - - # print('[]: local search for global path finished') - return result_path, result_distance - - @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 @@ -236,6 +176,14 @@ class MultipleAntColonySystem: for thread in ants_thread: thread.result() + # 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() + # 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: @@ -243,30 +191,20 @@ class MultipleAntColonySystem: 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)) - # 为global path进行局部搜索 - result_path, result_distance = MultipleAntColonySystem.local_search_procedure_for_global_path(new_graph, global_best_path, global_best_distance, stop_event) - - if result_distance is not None: - print('[acs_time]: local_search_procedure_for_global_path found a improved feasible path,' - ' send path info to macs') - path_found_queue.put(PathMessage(result_path, result_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): @@ -308,7 +246,7 @@ class MultipleAntColonySystem: for k in range(ants_num): ant = Ant(new_graph, 0) - thread = ants_pool.submit(MultipleAntColonySystem.new_active_ant, ant, vehicle_num, True, IN, q0, + thread = ants_pool.submit(MultipleAntColonySystem.new_active_ant, ant, vehicle_num, False, IN, q0, beta, stop_event) ants_thread.append(thread) @@ -352,6 +290,12 @@ class MultipleAntColonySystem: 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 @@ -388,6 +332,7 @@ class MultipleAntColonySystem: # 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') |