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import numpy as np
from vrptw_base import VPRTW_Graph, Ant
import random
class VRPTW_ACO:
def __init__(self, graph: VPRTW_Graph, ants_num=10, max_iter=100, alpha=1, beta=2, rho=0.2):
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
# alpha 信息素信息重要新
self.alpha = alpha
# beta 启发性信息重要性
self.beta = beta
# gamma 载重量信息重要性
self.gamma = 1
# lambda_ 行驶距离重要性
self.lambda_ = 1
# pheromone_rho 信息素挥发速度
self.pheromone_rho = rho
# q0 表示直接选择概率最大的下一点的概率
self.q0 = 0.1
# Q 表示每辆车的最大载重
self.max_load = graph.vehicle_capacity
# L 表示每辆车的最远行驶距离
self.max_travel_distance = 1000
# 出车的单位成本
self.car_cost = 50
# 行驶的单位成本
self.travel_cost = 1
# 早到时间成本
self.before_ready_time_cost = 1
# 晚到时间成本
self.after_due_time_cost = 2
# 信息素强度
self.Q = 1
# 车辆行驶速度
self.vehicle_speed = 60
# 创建信息素矩阵
self.pheromone_mat = self.init_pheromone_mat()
# 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
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):
self.pheromone_rho = self.calculate_pheromone_rho(iter)
self.q0 = self.calculate_prob_q0(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, self.vehicle_speed, next_index)
# 最终回到0位置
ants[k].move_to_next_index(self.graph, self.vehicle_speed, 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 or paths_distance[best_index] < self.best_path_distance:
self.best_path = ants[best_index].travel_path
self.best_path_distance = paths_distance[best_index]
print('[iteration %d]: best distance %f' % (iter, self.best_path_distance))
# 更新信息素表
self.update_pheromone_mat(ants, paths_distance)
def init_pheromone_mat(self):
"""
初始化信息素矩阵
随机地选择蚂蚁走的下一个位置，并且下一个需要满足载重、行驶距离等要求，直到走完所有的点，然后计算行走的路程L，则信息素初始化为1/(N*L)，这里的N为结点个数
:return:
"""
ant = Ant(self.graph.node_num)
while not ant.index_to_visit_empty():
next_index = random.choice(ant.index_to_visit)
if not self.check_condition(ant, next_index):
next_index = 0
ant.move_to_next_index(self.graph, self.vehicle_speed, next_index)
ant.move_to_next_index(self.graph, self.vehicle_speed, 0)
val = (1 / (self.graph.node_num * ant.total_travel_distance))
return np.ones((self.graph.node_num, self.graph.node_num)) * val
def calculate_pheromone_rho(self, iter):
"""
信息素挥发系数随着迭代次数进行改变
:param iter:
:return:
"""
if iter <= 1/3 * self.max_iter:
return 0.2
elif 1/3 * self.max_iter < iter < 2/3 * self.max_iter:
return 0.5
else:
return 0.8
def calculate_prob_q0(self, iter):
"""
q0着迭代次数进行改变
:param iter:
:return:
"""
return 0.1+0.8 * iter/self.max_iter
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
# 载重率信息
load_rating_mat = np.array([self.graph.nodes[ind].demand for ind in index_to_visit]) + ant.vehicle_load
load_rating_mat = load_rating_mat / self.max_load
# 行驶距离信息
distance_rating_mat = np.array([self.graph.node_dist_mat[current_index][ind] + self.graph.node_dist_mat[ind][0] for ind in index_to_visit])
distance_rating_mat = self.max_travel_distance / (ant.total_travel_distance + distance_rating_mat)
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) * \
np.power(load_rating_mat, self.gamma) * np.power(distance_rating_mat, self.lambda_)
if np.random.rand() < self.q0:
max_prob_index = np.argmax(transition_prob)
next_index = index_to_visit[max_prob_index]
else:
# 使用轮盘度
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
if ant.vehicle_travel_distance + self.graph.node_dist_mat[current_index][next_index] + self.graph.node_dist_mat[next_index][0] > self.max_travel_distance:
return False
return True
def update_pheromone_mat(self, ants, paths_distance):
"""
更新信息素矩阵
:return:
"""
self.pheromone_mat = (1 - self.pheromone_rho) * self.pheromone_mat
for k in range(self.ants_num):
current_index = ants[k].travel_path[0]
for index in ants[k].travel_path[1:]:
self.pheromone_mat[current_index][index] += self.Q / paths_distance[k]
def calculate_all_path_cost(self, ants):
"""
计算所有蚂蚁行走路径的cost
:param ants:
:return:
"""
# 注意路径是否是从0开始、以0结束的
costs = np.zeros(self.ants_num)
for k in range(self.ants_num):
path = ants[k].travel_path
current_index = path[0]
for index in path[1:]:
if index == 0:
costs[k] += self.car_cost + self.travel_cost * self.graph.node_dist_mat[current_index][index]
costs[k] += self.travel_cost * self.graph.node_dist_mat[current_index][index]
costs[k] += self.before_ready_time_cost * max(self.graph.nodes[index].ready_time-ants[k].arrival_time, 0) + \
self.after_due_time_cost * max(ants[k].arrival_time-self.graph.nodes[index].due_time, 0)
current_index = index
return costs
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/c101.txt'
graph = VPRTW_Graph(file_path)
vrptw = VRPTW_ACO(graph)
vrptw.run()
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