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import numpy as np
from vrptw_base import VrptwGraph, Ant, NearestNeighborHeuristic
import random
from vprtw_aco_figure import VrptwAcoFigure
class VrptwAco:
def __init__(self, graph: VrptwGraph, ants_num=10, max_iter=200, alpha=1, beta=2, rho=0.1):
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
# alpha 信息素信息重要新
self.alpha = alpha
# beta 启发性信息重要性
self.beta = beta
# rho 信息素挥发速度
self.rho = rho
# q0 表示直接选择概率最大的下一点的概率
self.q0 = 0.1
# 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
# 创建信息素矩阵
nn_heuristic = NearestNeighborHeuristic(self.graph)
self.init_pheromone_val = nn_heuristic.cal_init_pheromone_val()
self.pheromone_mat = np.ones((self.graph.node_num, self.graph.node_num)) * self.init_pheromone_val
# 启发式信息矩阵
self.heuristic_info_mat = 1 / graph.node_dist_mat
# 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.node_num) 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 self.check_condition(ants[k], next_index):
next_index = self.select_next_index(ants[k])
if not self.check_condition(ants[k], next_index):
next_index = 0
# 更新蚂蚁路径
ants[k].move_to_next_index(self.graph, next_index)
self.local_update_pheromone(ants[k].current_index, next_index)
# 最终回到0位置
ants[k].move_to_next_index(self.graph, 0)
self.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[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[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.global_update_pheromone()
def select_next_index(self, ant: 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.pheromone_mat[current_index][index_to_visit], self.alpha) * \
np.power(self.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 check_condition(self, ant: Ant, next_index) -> bool:
"""
检查移动到下一个点是否满足约束条件
:param ant:
:param next_index:
:return:
"""
current_index = ant.current_index
if ant.vehicle_load + self.graph.nodes[next_index].demand > self.max_load:
return False
# 检查访问某一个旅客之后，能否回到服务店
if ant.vehicle_travel_time + self.graph.node_dist_mat[current_index][next_index] + self.graph.node_dist_mat[next_index][0] > self.graph.nodes[0].due_time:
return False
# 不可以服务due time之外的旅客
if ant.vehicle_travel_time + self.graph.node_dist_mat[current_index][next_index] > self.graph.nodes[next_index].due_time:
return False
return True
def local_update_pheromone(self, start_ind, end_ind):
self.pheromone_mat[start_ind][end_ind] = (1-self.rho) * self.pheromone_mat[start_ind][end_ind] + \
self.rho * self.init_pheromone_val
def global_update_pheromone(self):
"""
更新信息素矩阵
:return:
"""
self.pheromone_mat = (1-self.rho) * self.pheromone_mat
current_ind = self.best_path[0]
for next_ind in self.best_path[1:]:
self.pheromone_mat[current_ind][next_ind] += self.Q/self.best_path_distance
current_ind = next_ind
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]
if __name__ == '__main__':
file_path = './solomon-100/r101.txt'
graph = VrptwGraph(file_path)
vrptw = VrptwAco(graph)
vrptw.run()
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