def run_basic_aco(self):
path_queue_for_figure = Queue()
basic_aco_thread = Thread(target=self._basic_aco, args=(path_queue_for_figure,))
basic_aco_thread.start()
if self.whether_or_not_to_show_figure:
figure = VrptwAcoFigure(self.graph.nodes, path_queue_for_figure)
figure.run()
basic_aco_thread.join()
if self.whether_or_not_to_show_figure:
path_queue_for_figure.put(PathMessage(None, None))
def _basic_aco(self, path_queue_for_figure: Queue):
"""
:return:
"""
start_time_total = time.time()
start_iteration = 0
for iter in range(self.max_iter):
ants = list(Ant(self.graph) for _ in range(self.ants_num))
for k in range(self.ants_num):
while not ants[k].index_to_visit_empty():
next_index = self.select_next_index(ants[k])
if not ants[k].check_condition(next_index):
next_index = self.select_next_index(ants[k])
if not ants[k].check_condition(next_index):
next_index = 0
ants[k].move_to_next_index(next_index)
self.graph.local_update_pheromone(ants[k].current_index, next_index)
ants[k].move_to_next_index(0)
self.graph.local_update_pheromone(ants[k].current_index, 0)
paths_distance = np.array([ant.total_travel_distance for ant in ants])
best_index = np.argmin(paths_distance)
if self.best_path is None or paths_distance[best_index] < self.best_path_distance:
self.best_path = ants[int(best_index)].travel_path
self.best_path_distance = paths_distance[best_index]
self.best_vehicle_num = self.best_path.count(0) - 1
start_iteration = iter
if self.whether_or_not_to_show_figure:
path_queue_for_figure.put(PathMessage(self.best_path, self.best_path_distance))
print('\n')
print('[iteration %d]: find a improved path, its distance is %f' % (iter, self.best_path_distance))
print('it takes %0.3f second multiple_ant_colony_system running' % (time.time() - start_time_total))
self.graph.global_update_pheromone(self.best_path, self.best_path_distance)
given_iteration = 100
if iter - start_iteration > given_iteration:
print('\n')
print('iteration exit: can not find better solution in %d iteration' % given_iteration)
break
print('\n')
print('final best path distance is %f, number of vehicle is %d' % (self.best_path_distance, self.best_vehicle_num))
print('it takes %0.3f second multiple_ant_colony_system running' % (time.time() - start_time_total))
def _multiple_ant_colony_system(self,
path_queue_for_figure: MPQueue,
file_to_write_path=None):
"""
: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()
global_path_to_acs_time = Queue()
global_path_to_acs_vehicle = Queue()
path_found_queue = Queue()
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()
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()
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))
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))
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():
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)
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 found_path_distance < self.best_path_distance:
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 self.whether_or_not_to_show_figure:
path_queue_for_figure.put(
PathMessage(self.best_path,
self.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 found_path_used_vehicle_num < best_vehicle_num:
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))
print('[macs]: send stop info to acs_time and acs_vehicle')
stop_event.set()
def _basic_aco(self, path_queue_for_figure: Queue):
"""
最基本的蚁群算法
:return:
"""
start_time_total = time.time()
# 最大迭代次数
start_iteration = 0
for iter in range(self.max_iter):
# 为每只蚂蚁设置当前车辆负载，当前旅行距离，当前时间
ants = list(Ant(self.graph) for _ in range(self.ants_num))
for k in range(self.ants_num):
# 蚂蚁需要访问完所有的客户
while not ants[k].index_to_visit_empty():
next_index = self.select_next_index(ants[k])
# 判断加入该位置后，是否还满足约束条件, 如果不满足，则再选择一次，然后再进行判断
if not ants[k].check_condition(next_index):
next_index = self.select_next_index(ants[k])
if not ants[k].check_condition(next_index):
next_index = 0
# 更新蚂蚁路径
ants[k].move_to_next_index(next_index)
self.graph.local_update_pheromone(ants[k].current_index,
next_index)
# 最终回到0位置
ants[k].move_to_next_index(0)
self.graph.local_update_pheromone(ants[k].current_index, 0)
# 计算所有蚂蚁的路径长度
paths_distance = np.array(
[ant.total_travel_distance for ant in ants])
# 记录当前的最佳路径
best_index = np.argmin(paths_distance)
if self.best_path is None or paths_distance[
best_index] < self.best_path_distance:
self.best_path = ants[int(best_index)].travel_path
self.best_path_distance = paths_distance[best_index]
self.best_vehicle_num = self.best_path.count(0) - 1
start_iteration = iter
# 图形化展示
if self.whether_or_not_to_show_figure:
path_queue_for_figure.put(
PathMessage(self.best_path, self.best_path_distance))
print('\n')
print(
'[iteration %d]: find a improved path, its distance is %f'
% (iter, self.best_path_distance))
print(
'it takes %0.3f second multiple_ant_colony_system running'
% (time.time() - start_time_total))
# 更新信息素表
self.graph.global_update_pheromone(self.best_path,
self.best_path_distance)
given_iteration = 100
if iter - start_iteration > given_iteration:
print('\n')
print(
'iteration exit: can not find better solution in %d iteration'
% given_iteration)
break
print('\n')
print('final best path distance is %f, number of vehicle is %d' %
(self.best_path_distance, self.best_vehicle_num))
print('it takes %0.3f second multiple_ant_colony_system running' %
(time.time() - start_time_total))
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):
"""
: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:
"""
print('[acs_vehicle]: start, vehicle_num %d' % vehicle_num)
global_best_path = None
global_best_distance = None
current_path, current_path_distance, _ = new_graph.nearest_neighbor_heuristic(
max_vehicle_num=vehicle_num)
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)
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
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
IN = np.zeros(new_graph.node_num)
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))
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 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):
"""
: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:
"""
print('[acs_time]: start, vehicle_num %d' % vehicle_num)
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)
for thread in ants_thread:
thread.result()
ant_best_travel_distance = None
ant_best_path = None
for ant in ants:
if stop_event.is_set():
print('[acs_time]: receive stop event')
return
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(
)
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
new_graph.global_update_pheromone(global_best_path,
global_best_distance)
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()
def _multiple_ant_colony_system(self, path_queue_for_figure: MPQueue, file_to_write_path=None):
"""
调用acs_time 和 acs_vehicle进行路径的探索
: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()
# 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
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)
# 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))
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
# 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()
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
# 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()
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))
# 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()
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(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)
# 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 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()
ant_best_travel_distance = None
ant_best_path = None
# 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()
# 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()