def acs_vehicle(new_graph, vehicle_num, ants_num, q0, beta, global_path_queue, path_found_queue, stop_event):
# vehicle_num is set to one less than the current best_path
print('[acs_vehicle]: start, vehicle_num %d' % vehicle_num)
global_best_path = None
global_best_distance = None
# Initialize path and distance using nearest_neighbor_heuristic algorithm
current_path, current_path_distance, _ = new_graph.nearest_neighbor(max_vehicle_num=vehicle_num)
# Find the unvisited nodes in the current path
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 and wait for the thread to finish
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
# The path found by the ant is compared with the current_path, can you use the vehicle_num
# vehicles to access 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 easy, it will 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(Path(ant.travel_path, ant.total_travel_distance))
# Update the 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 acs_time(new_graph, vehicle_num, ants_num, q0, beta, global_path_queue, path_found_queue, stop_event):
# You can use vehicle_num vehicles at most, that is, the path contains the most vehicle_num + 1
# depots to find the shortest path, 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 and wait for the thread to finish
for thread in ants_thread:
thread.result()
ant_best_travel_distance = None
ant_best_path = None
# Determine whether the path found by the ant is feasible and better than the global path
for ant in ants:
if stop_event.is_set():
print('[acs_time]: receive stop event')
return
# Get the current 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()
# The shortest path calculated by the ant
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
# Perform global update of pheromone here
new_graph.global_update_pheromone(global_best_path, global_best_distance)
# Send the calculated current best path to macs
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(Path(ant_best_path, ant_best_travel_distance))
ants_thread.clear()
for ant in ants:
ant.clear()
del ant
ants.clear()