from vrptw_base import VrptwGraph
from multiple_ant_colony_system import MultipleAntColonySystem
if __name__ == '__main__':
file_path = './solomon-100/c101.txt'
ants_num = 10
beta = 2
q0 = 0.1
show_figure = True
graph = VrptwGraph(file_path)
macs = MultipleAntColonySystem(graph,
ants_num=ants_num,
beta=beta,
q0=q0,
whether_or_not_to_show_figure=show_figure)
macs.run_multiple_ant_colony_system()
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()
from vrptw_base import VrptwGraph
from multiple_ant_colony_system import MultipleAntColonySystem
if __name__ == '__main__':
file_path = './solomon-100/c101.txt'
ants_num = 10
beta = 2
q0 = 0.1
show_figure = True
graph = VrptwGraph(file_path, mode=1)
macs = MultipleAntColonySystem(graph, ants_num=ants_num, beta=beta, q0=q0, mode=1, whether_or_not_to_show_figure = show_figure)
macs.run_multiple_ant_colony_system()
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()