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| import random import copy import time import sys import math import tkinter import threading from functools import reduce
''' 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) = (1.0,2.0,0.5,100.0)
(city_num, ant_num) = (50,50) distance_x = [ 178,272,176,171,650,499,267,703,408,437,491,74,532, 416,626,42,271,359,163,508,229,576,147,560,35,714, 757,517,64,314,675,690,391,628,87,240,705,699,258, 428,614,36,360,482,666,597,209,201,492,294] distance_y = [ 170,395,198,151,242,556,57,401,305,421,267,105,525, 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]
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)]
class Ant(object): def __init__(self,ID): self.ID = ID self.__clean_data() def __clean_data(self): self.path = [] self.total_distance = 0.0 self.move_count = 0 self.current_city = -1 self.open_table_city = [True for i in range(city_num)] city_index = random.randint(0,city_num-1) self.current_city = city_index self.path.append(city_index) self.open_table_city[city_index] = False self.move_count = 1 def __choice_next_city(self): next_city = -1 select_citys_prob = [0.0 for i in range(city_num)] total_prob = 0.0 for i in range(city_num): if self.open_table_city[i]: try : 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) if total_prob > 0.0: temp_prob = random.uniform(0.0, total_prob) for i in range(city_num): if self.open_table_city[i]: temp_prob -= select_citys_prob[i] if temp_prob < 0.0: next_city = i break if (next_city == -1): next_city = random.randint(0, city_num - 1) while ((self.open_table_city[next_city]) == False): next_city = random.randint(0, city_num - 1) return next_city 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] end = self.path[0] temp_distance += distance_graph[start][end] self.total_distance = temp_distance def __move(self, next_city): self.path.append(next_city) 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 def search_path(self): self.__clean_data() while self.move_count < city_num: next_city = self.__choice_next_city() self.__move(next_city) self.__cal_total_distance()
class TSP(object): def __init__(self, root, width = 800, height = 600, n = city_num): self.root = root self.width = width self.height = height self.n = n self.canvas = tkinter.Canvas( root, width = self.width, height = self.height, bg = "#EBEBEB", 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() self.__bindEvents() self.new() 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)) def __bindEvents(self): self.root.bind("q", self.quite) self.root.bind("n", self.new) self.root.bind("e", self.search_path) self.root.bind("s", self.stop) def title(self, s): self.root.title(s) def new(self, evt = None): self.__lock.acquire() self.__running = False self.__lock.release() self.clear() self.nodes = [] self.nodes2 = [] for i in range(len(distance_x)): x = distance_x[i] y = distance_y[i] self.nodes.append((x, y)) node = self.canvas.create_oval(x - self.__r, y - self.__r, x + self.__r, y + self.__r, fill = "#ff0000", outline = "#000000", tags = "node", ) self.nodes2.append(node) self.canvas.create_text(x,y-10, text = '('+str(x)+','+str(y)+')', fill = 'black' ) for i in range(city_num): for j in range(city_num): pheromone_graph[i][j] = 1.0 self.ants = [Ant(ID) for ID in range(ant_num)] self.best_ant = Ant(-1) self.best_ant.total_distance = 1 << 31 self.iter = 1 def line(self, order): self.canvas.delete("line") def line2(i1, i2): p1, p2 = self.nodes[i1], self.nodes[i2] self.canvas.create_line(p1, p2, fill = "#000000", tags = "line") return i2 reduce(line2, order, order[-1]) def clear(self): for item in self.canvas.find_all(): self.canvas.delete(item) def quite(self, evt): self.__lock.acquire() self.__running = False self.__lock.release() self.root.destroy() print (u"\n程序已退出...") sys.exit() def stop(self, evt): self.__lock.acquire() self.__running = False self.__lock.release() def search_path(self, evt = None): self.__lock.acquire() self.__running = True self.__lock.release() while self.__running: for ant in self.ants: ant.search_path() if ant.total_distance < self.best_ant.total_distance: self.best_ant = copy.deepcopy(ant) self.__update_pheromone_gragh() print (u"迭代次数:",self.iter,u"最佳路径总距离:",int(self.best_ant.total_distance)) self.line(self.best_ant.path) self.title("TSP蚁群算法(n:随机初始 e:开始搜索 s:停止搜索 q:退出程序) 迭代次数: %d" % self.iter) self.canvas.update() self.iter += 1 def __update_pheromone_gragh(self): temp_pheromone = [[0.0 for col in range(city_num)] for raw in range(city_num)] for ant in self.ants: for i in range(1,city_num): start, end = ant.path[i-1], ant.path[i] temp_pheromone[start][end] += Q / ant.total_distance temp_pheromone[end][start] = temp_pheromone[start][end] for i in range(city_num): for j in range(city_num): pheromone_graph[i][j] = pheromone_graph[i][j] * RHO + temp_pheromone[i][j] def mainloop(self): self.root.mainloop()
if __name__ == '__main__': TSP(tkinter.Tk()).mainloop()
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