diff options
| -rw-r--r-- | main.py | 99 | ||||
| -rw-r--r-- | vrptw_base.py | 60 |
2 files changed, 87 insertions, 72 deletions
@@ -1,12 +1,12 @@ import numpy as np -from vrptw_base import VPRTW_Graph, Ant +from vrptw_base import VrptwGraph, Ant, NearestNeighborHeuristic import random import matplotlib.pyplot as plt import time -class VRPTW_ACO: - def __init__(self, graph: VPRTW_Graph, ants_num=10, max_iter=100, alpha=1, beta=3, rho=0.2): +class VrptwAco: + def __init__(self, graph: VrptwGraph, ants_num=10, max_iter=400, alpha=1, beta=2, rho=0.1): super() # graph 结点的位置、服务时间信息 self.graph = graph @@ -18,31 +18,25 @@ class VRPTW_ACO: self.alpha = alpha # beta 启发性信息重要性 self.beta = beta - # pheromone_rho 信息素挥发速度 - self.pheromone_rho = rho + # 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 - # Q 表示每辆车的最大载重 + # vehicle_capacity 表示每辆车的最大载重 self.max_load = graph.vehicle_capacity - # 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 # 创建信息素矩阵 - self.pheromone_mat = self.init_pheromone_mat() + 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 + self.whether_or_not_to_show_figure = True if self.whether_or_not_to_show_figure: # figure @@ -73,9 +67,11 @@ class VRPTW_ACO: # 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]) @@ -96,25 +92,7 @@ class VRPTW_ACO: 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, next_index) - ant.move_to_next_index(self.graph, 0) - - val = (1 / (self.graph.node_num * ant.total_travel_distance)) - return np.ones((self.graph.node_num, self.graph.node_num)) * val + self.global_update_pheromone() def select_next_index(self, ant: Ant): """ @@ -147,42 +125,31 @@ class VRPTW_ACO: if ant.vehicle_load + self.graph.nodes[next_index].demand > self.max_load: return False - # 在数据集中,时间信息已经被换成了距离信息 - if ant.vehicle_travel_distance + self.graph.node_dist_mat[current_index][next_index] + self.graph.node_dist_mat[next_index][0] > self.graph.nodes[0].due_time: + # 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 update_pheromone_mat(self, ants, paths_distance): + 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.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] + self.pheromone_mat = (1-self.rho) * self.pheromone_mat - 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 + 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): """ @@ -224,8 +191,8 @@ class VRPTW_ACO: if __name__ == '__main__': - file_path = './solomon-100/r101.txt' - graph = VPRTW_Graph(file_path) + file_path = './solomon-100/c101.txt' + graph = VrptwGraph(file_path) - vrptw = VRPTW_ACO(graph) + vrptw = VrptwAco(graph) vrptw.run() diff --git a/vrptw_base.py b/vrptw_base.py index 8de14ce..a4c92a1 100644 --- a/vrptw_base.py +++ b/vrptw_base.py @@ -13,7 +13,7 @@ class Node: self.service_time = service_time -class VPRTW_Graph: +class VrptwGraph: def __init__(self, file_path): super() # node_num 结点个数 @@ -45,7 +45,7 @@ class VPRTW_Graph: node_dist_mat[i][i] = np.inf for j in range(i+1, node_num): node_b = nodes[j] - node_dist_mat[i][j] = VPRTW_Graph.calculate_dist(node_a, node_b) + node_dist_mat[i][j] = VrptwGraph.calculate_dist(node_a, node_b) node_dist_mat[j][i] = node_dist_mat[i][j] return node_num, nodes, node_dist_mat, vehicle_num, vehicle_capacity @@ -60,7 +60,7 @@ class Ant: super() self.current_index = 0 self.vehicle_load = 0 - self.vehicle_travel_distance = 0 + self.vehicle_travel_time = 0 self.travel_path = [0] self.arrival_time = [0] self.index_to_visit = list(range(1, node_num)) @@ -69,21 +69,69 @@ class Ant: def move_to_next_index(self, graph, next_index): # 更新蚂蚁路径 self.travel_path.append(next_index) - self.arrival_time.append(self.vehicle_travel_distance) self.total_travel_distance += graph.node_dist_mat[self.current_index][next_index] + dist = graph.node_dist_mat[self.current_index][next_index] + self.arrival_time.append(self.vehicle_travel_time + dist) + if next_index == 0: # 如果一下个位置为服务器点，则要将车辆负载等清空 self.vehicle_load = 0 - self.vehicle_travel_distance = 0 + self.vehicle_travel_time = 0 else: # 更新车辆负载、行驶距离、时间 self.vehicle_load += graph.nodes[next_index].demand - self.vehicle_travel_distance += graph.node_dist_mat[self.current_index][next_index] + graph.nodes[next_index].service_time + # 如果早于客户要求的时间窗(ready_time)，则需要等待 + + self.vehicle_travel_time += dist + max(graph.nodes[next_index].ready_time - self.vehicle_travel_time - dist, 0) + graph.nodes[next_index].service_time self.index_to_visit.remove(next_index) self.current_index = next_index def index_to_visit_empty(self): return len(self.index_to_visit) == 0 + + +class NearestNeighborHeuristic: + def __init__(self, graph: VrptwGraph): + self.graph = graph + self.ant = Ant(graph.node_num) + + def cal_init_pheromone_val(self): + while not self.ant.index_to_visit_empty(): + customers_meet_constrains = self.cal_customers_meet_constrains() + if len(customers_meet_constrains) == 0: + next_index = 0 + else: + next_index = self.cal_nearest_customer(customers_meet_constrains) + + self.ant.move_to_next_index(self.graph, next_index) + self.ant.move_to_next_index(self.graph, 0) + + init_pheromone = (1 / (self.graph.node_num * self.ant.total_travel_distance)) + return init_pheromone + + def cal_customers_meet_constrains(self): + customers_meet_constrains = [] + current_ind = self.ant.current_index + for next_ind in self.ant.index_to_visit: + condition1 = self.ant.vehicle_travel_time + self.graph.node_dist_mat[current_ind][next_ind] <= self.graph.nodes[next_ind].due_time + condition2 = self.ant.vehicle_load + self.graph.nodes[next_ind].demand <= self.graph.vehicle_capacity + if condition1 and condition2: + customers_meet_constrains.append(next_ind) + return customers_meet_constrains + + def cal_nearest_customer(self, customers): + current_ind = self.ant.current_index + + nearest_ind = customers[0] + min_dist = self.graph.node_dist_mat[current_ind][customers[0]] + + for next_ind in customers[1:]: + dist = self.graph.node_dist_mat[current_ind][next_ind] + if dist < min_dist: + min_dist = dist + nearest_ind = next_ind + + return nearest_ind |