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import numpy as np
import random
from vprtw_aco_figure import VrptwAcoFigure
from vrptw_base import VrptwGraph
from ant import Ant
class VrptwAco:
def __init__(self, graph: VrptwGraph, ants_num=10, max_iter=200, alpha=1, beta=2):
super()
# graph 结点的位置、服务时间信息
self.graph = graph
# ants_num 蚂蚁数量
self.ants_num = ants_num
# max_iter 最大迭代次数
self.max_iter = max_iter
# vehicle_capacity 表示每辆车的最大载重
self.max_load = graph.vehicle_capacity
# 信息素强度
self.Q = 1
# alpha 信息素信息重要新
self.alpha = alpha
# beta 启发性信息重要性
self.beta = beta
# q0 表示直接选择概率最大的下一点的概率
self.q0 = 0.1
# best path
self.best_path_distance = None
self.best_path = None
self.whether_or_not_to_show_figure = False
if self.whether_or_not_to_show_figure:
# figure
self.figure = VrptwAcoFigure(self.graph)
def run(self):
"""
运行蚁群优化算法
:return:
"""
# 最大迭代次数
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:
self.best_path = ants[int(best_index)].travel_path
self.best_path_distance = paths_distance[best_index]
if self.whether_or_not_to_show_figure:
self.figure.init_figure(self.best_path)
elif 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]
if self.whether_or_not_to_show_figure:
self.figure.update_figure(self.best_path)
print('[iteration %d]: best distance %f' % (iter, self.best_path_distance))
# 更新信息素表
self.graph.global_update_pheromone(self.best_path, self.best_path_distance)
def select_next_index(self, ant):
"""
选择下一个结点
:param ant:
:return:
"""
current_index = ant.current_index
index_to_visit = ant.index_to_visit
transition_prob = np.power(self.graph.pheromone_mat[current_index][index_to_visit], self.alpha) * \
np.power(self.graph.heuristic_info_mat[current_index][index_to_visit], self.beta)
if np.random.rand() < self.q0:
max_prob_index = np.argmax(transition_prob)
next_index = index_to_visit[max_prob_index]
else:
# 使用轮盘赌算法
next_index = self.stochastic_accept(index_to_visit, transition_prob)
return next_index
def stochastic_accept(self, index_to_visit, transition_prob):
"""
轮盘赌
:param index_to_visit: a list of N index (list or tuple)
:param transition_prob:
:return: selected index
"""
# calculate N and max fitness value
N = len(index_to_visit)
# normalize
sum_tran_prob = np.sum(transition_prob)
norm_transition_prob = transition_prob/sum_tran_prob
# select: O(1)
while True:
# randomly select an individual with uniform probability
ind = int(N * random.random())
if random.random() <= norm_transition_prob[ind]:
return index_to_visit[ind]
def new_active_ant(self, ant: Ant, local_search: bool, IN):
# 计算从当前位置可以达到的下一个位置
next_index_meet_constrains = ant.cal_next_index_meet_constrains()
while len(next_index_meet_constrains) > 0:
index_num = len(next_index_meet_constrains)
ready_time = np.zeros(index_num)
due_time = np.zeros(index_num)
for i in range(index_num):
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.max(ant.vehicle_travel_time + ant.graph.node_dist_mat[ant.current_index][next_index_meet_constrains], ready_time)
delat_time = delivery_time - ant.vehicle_travel_time
distance = delat_time * (due_time - ant.vehicle_travel_time)
distance = np.max(1.0, distance-IN)
closeness = 1/distance
# 按照概率选择下一个点next_index
if np.random.rand() < ant.graph.q0:
max_prob_index = np.argmax(closeness)
next_index = next_index_meet_constrains[max_prob_index]
else:
# 使用轮盘赌算法
next_index = ant.graph.stochastic_accept(next_index_meet_constrains, closeness)
ant.move_to_next_index(next_index)
# 更新信息素矩阵
# 重新计算可选的下一个点
next_index_meet_constrains = ant.cal_next_index_meet_constrains()
ant.insertion_procedure()
# ant.index_to_visit_empty()==True就是feasible的意思
if local_search is True and ant.index_to_visit_empty():
new_path = ant.local_search_procedure()
if new_path is not None:
pass
def acs_time(self, vehicle_num):
# how to calculate init_pheromone_val
new_graph = self.graph.construct_graph_with_duplicated_depot(vehicle_num, 1)
# 初始化信息素矩阵
while True:
for k in range(self.ants_num):
ant = Ant(new_graph, random.randint(0, vehicle_num-1))
self.new_active_ant(ant, True, 0)
# if ant.index_to_visit_empty() and ant.total_travel_distance < global_travel_distance:
# send ant.travel_path
# pass
if __name__ == '__main__':
file_path = './solomon-100/c101.txt'
graph = VrptwGraph(file_path)
vrptw = VrptwAco(graph)
vrptw.run()
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