python_最基础的实现——蚁群算法

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34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， = 34， = 32；
操作系统：Windows 10；
实现语言：Python 3.7 ；
运行工具：Anaconda/Pycharm；
最终迭代最优路径：3419 ‍

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40-50次左右就达到最优了，，）



咳咳，，，附上这不讲究颗粒度的代码！嘿嘿

```python
"""
Created on Wed May 15 18:50:04 2019
@author: hp
"""
import random
import copy
import sys
import tkinter  # //GUI模块(引用tk模块)
import threading
from functools import reduce

# 参数说明
'''
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) = (34,32)
distance_x = [
    688,805,176,654,600,499,267,703,408,437,491,74,532,
    416,666,100,251,359,685,508,229,576,777,560,35,714,
    757,517,800,314,675,680,900,625]
distance_y = [
    300,325,198,500,242,600,57,401,305,421,222,105,525,
    381,244,350,395,169,625,380,153,442,268,329,232,40,
    498,265,100,120,165,50,180,550]

#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"最优路径所得总距离：",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 -----------

if __name__ == '__main__':
    print(u""" 
--------------------------------------------------------
    程序：蚁群算法解决简单TSP问题 
    作者：_jiao 
    日期：2019-05-15
    语言：Python 
-------------------------------------------------------- 
    """)
    TSP(tkinter.Tk()).mainloop()

最后，该问题迭代的最优路径为：3419。

半年后发出来这么简单的，，，emm…仅供参考~
虽说是上半年看了几个月的算法，找了好多篇帖子，写了篇论文，但还是感觉自己脑子一片空白
好了，不瞎扯了