diff options
Diffstat (limited to 'basic_aco.py')
| -rw-r--r-- | basic_aco.py | 18 |
1 files changed, 13 insertions, 5 deletions
diff --git a/basic_aco.py b/basic_aco.py index 3bdc24c..bbadb52 100644 --- a/basic_aco.py +++ b/basic_aco.py @@ -8,7 +8,7 @@ from queue import Queue class BasicACO: - def __init__(self, graph: VrptwGraph, ants_num=10, max_iter=200, alpha=1, beta=2): + def __init__(self, graph: VrptwGraph, ants_num=10, max_iter=200, beta=2): super() # graph 结点的位置、服务时间信息 self.graph = graph @@ -20,8 +20,6 @@ class BasicACO: 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 @@ -41,7 +39,7 @@ class BasicACO: # 是否要展示figure if self.whether_or_not_to_show_figure: - figure = VrptwAcoFigure(self.graph, path_queue_for_figure) + figure = VrptwAcoFigure(self.graph.nodes, path_queue_for_figure) figure.run() basic_aco_thread.join() @@ -55,6 +53,7 @@ class BasicACO: :return: """ # 最大迭代次数 + start_iteration = 0 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 @@ -86,12 +85,16 @@ class BasicACO: if self.best_path is None: self.best_path = ants[int(best_index)].travel_path self.best_path_distance = paths_distance[best_index] + + # 图形化展示 if self.whether_or_not_to_show_figure: path_queue_for_figure.put(PathMessage(self.best_path, self.best_path_distance)) 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] + + # 图形化展示 if self.whether_or_not_to_show_figure: path_queue_for_figure.put(PathMessage(self.best_path, self.best_path_distance)) @@ -99,6 +102,10 @@ class BasicACO: # 更新信息素表 self.graph.global_update_pheromone(self.best_path, self.best_path_distance) + given_iteration = 50 + if iter - start_iteration > given_iteration: + print('iteration is up: can not find better solution in %d iteration' % given_iteration) + def select_next_index(self, ant): """ 选择下一个结点 @@ -108,8 +115,9 @@ class BasicACO: 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) * \ + transition_prob = self.graph.pheromone_mat[current_index][index_to_visit] * \ np.power(self.graph.heuristic_info_mat[current_index][index_to_visit], self.beta) + transition_prob = transition_prob / np.sum(transition_prob) if np.random.rand() < self.q0: max_prob_index = np.argmax(transition_prob) |