def new_active_ant(ant: Ant, vehicle_num: int, local_search: bool,
IN: np.numarray, q0: float, beta: int,
stop_event: Event):
"""
: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])
unused_depot_count = vehicle_num
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
next_index_meet_constrains = ant.cal_next_index_meet_constrains()
if len(next_index_meet_constrains) == 0:
ant.move_to_next_index(0)
unused_depot_count -= 1
continue
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)
if np.random.rand() < q0:
max_prob_index = np.argmax(transition_prob)
next_index = next_index_meet_constrains[max_prob_index]
else:
next_index = MultipleAntColonySystem.stochastic_accept(
next_index_meet_constrains, transition_prob)
ant.graph.local_update_pheromone(ant.current_index, next_index)
ant.move_to_next_index(next_index)
if ant.index_to_visit_empty():
ant.graph.local_update_pheromone(ant.current_index, 0)
ant.move_to_next_index(0)
ant.insertion_procedure(stop_event)
if local_search is True and ant.index_to_visit_empty():
ant.local_search_procedure(stop_event)
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.graph.local_update_pheromone(ant.current_index, 0)
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)
