python_最基础的实现——蚁群算法
原标题:python_最基础的实现——蚁群算法
原文来自:CSDN 原文链接:https://blog.csdn.net/qq_42464635/article/details/103009744
为了更好地体现算法性能,采用Python来简单模拟TSP(旅行商问题),进而分析。已知32个城市、32只蚂蚁和两两城市间的距离,确定一条经过所有城市且仅一次的最短路径。
初始值设置:ρ=0.5,Q=100,α= 1.0,β=2.0;
操作系统:Windows 8.1;
实现语言:Python 3.6 ;
附:基本蚁群算法流程图
附:算法简单实现效果图(实在过于粗糙hhh)
算法初始——>迭代一次——>迭代100次(其实此实现代码迭代20~30次左右就达到最优了,,)
咳咳,,,附上这不讲究颗粒度的代码!
```python # -*- coding: utf-8 -*- """ Created on Wed May 15 18:50:04 2019 @author: hp """ import random import copy '''import time''' import sys '''import math''' import tkinter #//GUI模块(引用tk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) = (1.0,2.0,0.5,100.0) # 城市数,蚁群 (city_num, ant_num) = (32,32) distance_x = [ 178,272,176,654,600,499,267,703,408,437,491,74,532, 416,666,42,251,359,163,508,229,576,777,560,35,714, 757,517,800,314,675,680] distance_y = [ 170,380,198,500,242,556,57,401,305,421,222,105,525, 381,244,330,395,169,141,380,153,442,268,329,232,40, 498,265,100,120,165,50] #城市距离和信息素,创建二维数组(矩阵) 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)] #----------- 算法 ----------- class Ant(object): # 初始化 def __init__(self,ID): self.ID = ID # ID self.__clean_data() # 随机初始化出生点 # 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 # 当前停留的城市 self.open_table_city = [True for i in range(city_num)] # 探索城市的状态 city_index = random.randint(0,city_num-1) # 随机初始出生点 self.current_city = city_index self.path.append(city_index) self.open_table_city[city_index] = False self.move_count = 1 # 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) # 轮盘调度选择城市 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 if (next_city == -1): next_city = random.randint(0, city_num - 1) while ((self.open_table_city[next_city]) == False): # if==False,说明已经遍历过了 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.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 # 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 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 # 城市数目初始化为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(i: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() # 计算城市之间的距离 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("i", self.new) # 初始化程序 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) # 初始化 def new(self, evt = None): # 停止线程 self.__lock.acquire() self.__running = False self.__lock.release() self.clear() # 清除信息 self.nodes = [] # 节点坐标 self.nodes2 = [] # 节点对象 # 初始化城市节点 for i in range(len(distance_x)): # 在画布上随机初始坐标 x = distance_x[i] y = distance_y[i] self.nodes.append((x, y)) # 生成节点椭圆,半径为self.__r node = self.canvas.create_oval(x - self.__r, y - self.__r, x + self.__r, y + self.__r, fill = "#0000FF", # 填充蓝色 outline = "#000000", # 轮廓白色 tags = "node", ) self.nodes2.append(node) # 显示坐标 self.canvas.create_text(x,y-10, # 使用create_text方法在坐标(302,77)处绘制文字 text = '('+str(x)+','+str(y)+')', # 所绘制文字的内容 fill = 'black' # 所绘制文字的颜色为灰色 ) # 顺序连接城市 #self.line(range(city_num)) # 初始城市之间的距离和信息素 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)] # 初始蚁群 self.best_ant = Ant(-1) # 初始最优解 self.best_ant.total_distance = 1 << 31 # 初始最大距离 self.iter = 1 # 初始化迭代次数 # 将节点按order顺序连线 def line(self, order): # 删除原线 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 # order[-1]为初始值 reduce(line2, order, order[-1]) # 清除画布 def clear(self): for item in self.canvas.find_all(): self.canvas.delete(item) # 退出程序 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() # 停止搜索 def stop(self, evt): self.__lock.acquire() self.__running = False self.__lock.release() # 开始搜索 def search_path(self, evt = None) # 开启线程 self.__lock.acquire() self.__running = True self.__lock.release() while self.__running: # 遍历每一只蚂蚁 for ant in self.ants: # 搜索一条路径 ant.search_path() # 与当前最优蚂蚁比较 if ant.total_distance < self.best_ant.total_distance: # 更新最优解 self.best_ant = copy.deepcopy(ant) # 更新信息素 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)) # 连线 self.line(self.best_ant.path) # 设置标题 self.title("TSP蚁群算法(i: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) # 更新画布 self.canvas.update() self.iter += 1 # 更新信息素 def __update_pheromone_gragh(self): # 获取每只蚂蚁在其路径上留下的信息素 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] # 在路径上的每两个相邻城市间留下信息素,与路径总距离反比 temp_pheromone[start][end] += Q / ant.total_distance temp_pheromone[end][start] = temp_pheromone[start][end] # 更新所有城市之间的信息素,旧信息素衰减加上新迭代信息素 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] # 主循环 def mainloop(self): self.root.mainloop() #----------- 程序的入口处 ----------- if __name__ == '__main__': print (u""" -------------------------------------------------------- 程序:蚁群算法解决TSP问题程序 作者:_闰土的猹 日期:2019-05-15 语言:Python -------------------------------------------------------- """) TSP(tkinter.Tk()).mainloop()
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