基于深度学习的共享单车预测与调度解决方案.zip

# -*- coding: utf-8 -*- import random import copy import time import sys import math import tkinter # //GUI模块 import threading from functools import reduce # 参数 ''' ALPHA:信息启发因子，值越大，则蚂蚁选择之前走过的路径可能性就越大 ，值越小，则蚁群搜索范围就会减少，容易陷入局部最优 BETA:Beta值越大，蚁群越就容易选择局部较短路径，这时算法收敛速度会 加快，但是随机性不高，容易得到局部的相对最优 ''' (ALPHA, BETA, RHO, Q) = (1.0, 1.0, 0.5, 100.0) # 站点数，蚁群 # 默认货车从数组中的第一个站点发出 (city_num, ant_num) = (50, 50) distance_x = [ 158, 272, 176, 91, 550, 499, 267, 703, 408, 437, 431, 74, 532, 416, 626, 42, 221, 359, 163, 508, 229, 576, 147, 560, 115, 654, 757, 517, 64, 314, 675, 690, 391, 628, 87, 140, 705, 699, 258, 428, 614, 36, 360, 482, 666, 597, 209, 201, 492, 294] distance_y = [ 90, 395, 198, 131, 242, 556, 57, 401, 305, 421, 267, 105, 525, 381, 244, 330, 395, 169, 141, 380, 153, 442, 528, 329, 232, 48, 498, 265, 343, 120, 165, 50, 433, 63, 491, 275, 348, 222, 288, 490, 213, 524, 244, 114, 104, 552, 70, 425, 227, 331] #bi为实际单车数-单车需求量的偏差值，bi>0供大于求->装上货车,bi<0供不应求->卸下货车 bi=[ 0,23,-4,17,-15,-9,21,20,-8,-30,-2,-3,2,-6,8,4,-2,-6,8,13,20,-6,3,-18,-29, 5,20,14,-6,1,-18,-3,-7,1,6,-3,9,4,17,25,23,10,-25,9,5,-16,8,1,-25,4] #MaxBike为货车最大负载量 MaxBike=100 # 站点距离和信息素 distance_graph = [[0.0 for col in range(city_num)] for raw in range(city_num)] pheromone_graph = [[1.0 for col in range(city_num)] for raw in range(city_num)] # ----------- 蚂蚁 ----------- class Ant(object): # 初始化 def __init__(self, ID): self.ID = ID # ID self.__clean_data() # 初始化蚂蚁 # 初始数据 def __clean_data(self): self.path = [] # 当前蚂蚁的路径 self.total_distance = 0.0 # 当前路径的总距离 self.move_count = 0 # 移动次数 self.current_city = -1 # 当前停留的站点 self.CurrentBike=0 # 当前货车上的单车数 self.not_visited_city = [True for i in range(city_num)] #站点是否已经访问过 self.open_table_city = [True for i in range(city_num)] # 探索站点的状态 city_index = 0 # 初始出生点为第一个站点 self.current_city = city_index self.path.append(city_index) self.not_visited_city[city_index] = False self.move_count = 1 self.__calculate_open_table_city() #计算满足约束条件的备选列表 def __calculate_open_table_city(self): for i in range(len(self.open_table_city)): if self.not_visited_city[i]==False:#过滤掉已经访问过的结点 self.open_table_city[i]=False else: if (bi[i]>=0 and self.CurrentBike+bi[i]<=MaxBike) or (bi[i]<=0 and self.CurrentBike+bi[i]>=0): self.open_table_city[i]=True else: self.open_table_city[i] = False # 选择下一个城市 def __choice_next_city(self): next_city = -1 select_citys_prob = [0.0 for i in range(city_num)] # 存储去下个城市的概率 total_prob = 0.0 # 获取去下一个城市的概率 for i in range(city_num): if self.open_table_city[i]: try: # 计算概率：与信息素浓度成正比，与距离成反比 select_citys_prob[i] = pow(pheromone_graph[self.current_city][i], ALPHA) * pow( (1.0 / distance_graph[self.current_city][i]), BETA) total_prob += select_citys_prob[i] except ZeroDivisionError as e: print('Ant ID: {ID}, current city: {current}, target city: {target}'.format(ID=self.ID, current=self.current_city, target=i)) sys.exit(1) # 轮盘选择城市 if total_prob > 0.0: # 产生一个随机概率,0.0-total_prob temp_prob = random.uniform(0.0, total_prob) for i in range(city_num): if self.open_table_city[i]: # 轮次相减 temp_prob -= select_citys_prob[i] if temp_prob < 0.0: next_city = i break # 未从概率产生，顺序选择一个未访问城市 # if next_city == -1: # for i in range(city_num): # if self.open_table_city[i]: # next_city = i # break if (next_city == -1): next_city = random.randint(0, city_num - 1) while ((self.open_table_city[next_city]) == False): # if==False,说明不可选择该站点 next_city = random.randint(0, city_num - 1) # 返回下一个城市序号 return next_city # 计算路径总距离 def __cal_total_distance(self): temp_distance = 0.0 for i in range(1, city_num): start, end = self.path[i], self.path[i - 1] temp_distance += distance_graph[start][end] # 回路 end = self.path[0] temp_distance += distance_graph[start][end] self.total_distance = temp_distance # 移动操作 def __move(self, next_city): self.path.append(next_city) self.not_visited_city[next_city] = False self.open_table_city[next_city] = False self.total_distance += distance_graph[self.current_city][next_city] self.current_city = next_city self.move_count += 1 self.CurrentBike+=bi[next_city] # 搜索路径 def search_path(self): # 初始化数据 self.__clean_data() # 搜素路径，遍历完所有城市为止 while self.move_count < city_num: # 移动到下一个城市 self.__calculate_open_table_city() next_city = self.__choice_next_city() self.__move(next_city) # 计算路径总长度 self.__cal_total_distance() # ----------- TSP问题 ----------- class TSP(object): def __init__(self, root, width=800, height=600, n=city_num): # 创建画布 self.root = root self.width = width self.height = height # 站点数目初始化为city_num self.n = n # tkinter.Canvas self.canvas = tkinter.Canvas( root, width=self.width, height=self.height, bg="#EBEBEB", # 背景白色 xscrollincrement=1, yscrollincrement=1 ) self.canvas.pack(expand=tkinter.YES, fill=tkinter.BOTH) self.title("TSP蚁群算法(n:初始化 e:开始搜索 s:停止搜索 q:退出程序)") self.__r = 5 self.__lock = threading.RLock() # 线程锁 self.__bindEvents() self.new() # 计算站点之间的距离 for i in range(city_num): for j in range(city_num): temp_distance = pow((distance_x[i] - distance_x[j]), 2) + pow((distance_y[i] - distance_y[j]), 2) temp_distance = pow(temp_distance, 0.5) distance_graph[i][j] = float(int(temp_distance + 0.5)) # 按键响应程序 def __bindEvents(self): self.root.bind("q", self.quite) # 退出程序 self.root.bind("n", self.new) # 初始化 self.root.bind("e", self.search_path) # 开始搜索 self.root.bind("s", self.stop) # 停止搜索 # 更改标题 def title(self, s): self.root.title(s) # 初始�