為了更好地展現算法性能,采用Python來簡單模拟TSP(旅行商問題),進而分析。已知34個城市、32隻螞蟻和兩兩城市間的距離,确定一條經過所有城市且僅一次的最短路徑。
初始值設定:ρ = 0.5,Q = 100,α = 1.0,β = 2.0,🏙 = 34,🐜 = 32;
作業系統:Windows 10;
實作語言:Python 3.7 ;
運作工具:Anaconda/Pycharm;
最終疊代最優路徑:3419 🐱🏍
附:基本蟻群算法流程圖(圖是照着書上畫的哈哈哈哈)
附:算法簡單實作效果圖(實在過于粗糙hhh)
算法初始——>疊代一次——>疊代100次(其實此實作代碼疊代40-50次左右就達到最優了,,)
咳咳,,,附上這不講究顆粒度的代碼!嘿嘿😄
```python
"""
Created on Wed May 15 18:50:04 2019
@author: hp
"""
import random
import copy
import sys
import tkinter # //GUI子產品(引用tk子產品)
import threading
from functools import reduce
# 參數說明
'''
ALPHA:資訊啟發因子,值越大,則螞蟻選擇之前走過的路徑可能性就越大
,值越小,則蟻群搜尋範圍就會減少,容易陷入局部最優;
BETA:Beta值越大,蟻群越就容易選擇局部較短路徑,這時算法收斂速度會
加快,但是随機性不高,容易得到局部的相對最優.
'''
(ALPHA, BETA, RHO, Q) = (1.0,2.0,0.5,100.0)
# 城市數,蟻群
(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]
#城市距離和資訊素,建立二維數組(矩陣)
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() # 随機初始化出生點
# 初始資料
def __clean_data(self):
self.path = [] # 目前螞蟻的路徑
self.total_distance = 0.0 # 目前路徑的總距離
self.move_count = 0 # 移動次數
self.current_city = -1 # 目前停留的城市
self.open_table_city = [True for i in range(city_num)] # 探索城市的狀态
city_index = random.randint(0,city_num-1) # 随機初始出生點
self.current_city = city_index
self.path.append(city_index)
self.open_table_city[city_index] = False
self.move_count = 1
# 選擇下一個城市
def __choice_next_city(self):
next_city = -1
select_citys_prob = [0.0 for i in range(city_num)] # 存儲去下個城市的機率
total_prob = 0.0
# 擷取去下一個城市的機率
for i in range(city_num):
if self.open_table_city[i]:
try:
# 計算機率:與資訊素濃度成正比,與距離成反比
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)
# 輪盤排程選擇城市
if total_prob > 0.0:
# 産生一個随機機率,0.0-total_prob
temp_prob = random.uniform(0.0, total_prob)
for i in range(city_num):
if self.open_table_city[i]:
# 輪次相減
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,說明已經周遊過了
next_city = random.randint(0, city_num - 1)
# 傳回下一個城市序号
return next_city
# 計算路徑總距離
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
# 移動操作
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
# 搜尋路徑
def search_path(self):
# 初始化資料
self.__clean_data()
# 搜素路徑,周遊完所有城市為止
while self.move_count < city_num:
# 移動到下一個城市
next_city = self.__choice_next_city()
self.__move(next_city)
# 計算路徑總長度
self.__cal_total_distance()
#----------- TSP問題 -----------
class TSP(object):
def __init__(self, root, width=800, height=600, n=city_num):
# 建立畫布
self.root = root
self.width = width
self.height = height
# 城市數目初始化為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蟻群算法(i:初始化 e:開始搜尋 s:停止搜尋 q:退出程式)")
self.__r = 5
self.__lock = threading.RLock() # 線程鎖
self.__bindEvents()
self.new()
# 計算城市之間的距離
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))
# 按鍵響應程式
def __bindEvents(self):
self.root.bind("q", self.quite) # 退出程式
self.root.bind("i", self.new) # 初始化程式
self.root.bind("e", self.search_path) # 開始搜尋
self.root.bind("s", self.stop) # 停止搜尋
# 更改标題
def title(self, s):
self.root.title(s)
# 初始化
def new(self, evt=None):
# 停止線程
self.__lock.acquire()
self.__running = False
self.__lock.release()
self.clear() # 清除資訊
self.nodes = [] # 節點坐标
self.nodes2 = [] # 節點對象
# 初始化城市節點
for i in range(len(distance_x)):
# 在畫布上随機初始坐标
x = distance_x[i]
y = distance_y[i]
self.nodes.append((x, y))
# 生成節點橢圓,半徑為self.__r
node = self.canvas.create_oval(x - self.__r,
y - self.__r, x + self.__r, y + self.__r,
fill="#0000FF", # 填充藍色
outline="#000000", # 輪廓白色
tags="node",
)
self.nodes2.append(node)
# 顯示坐标
self.canvas.create_text(x,y-10, # 使用create_text方法在坐标(302,77)處繪制文字
text='('+str(x)+','+str(y)+')', # 所繪制文字的内容
fill='black' # 所繪制文字的顔色為灰色
)
# 順序連接配接城市
#self.line(range(city_num))
# 初始城市之間的距離和資訊素
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)] # 初始蟻群
self.best_ant = Ant(-1) # 初始最優解
self.best_ant.total_distance = 1 << 31 # 初始最大距離
self.iter = 1 # 初始化疊代次數
# 将節點按order順序連線
def line(self, order):
# 删除原線
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]為初始值
reduce(line2, order, order[-1])
# 清除畫布
def clear(self):
for item in self.canvas.find_all():
self.canvas.delete(item)
# 退出程式
def quite(self, evt):
self.__lock.acquire()
self.__running = False
self.__lock.release()
self.root.destroy()
print(u"\n程式已退出...")
sys.exit()
# 停止搜尋
def stop(self, evt):
self.__lock.acquire()
self.__running = False
self.__lock.release()
# 開始搜尋
def search_path(self, evt=None):
# 開啟線程
self.__lock.acquire()
self.__running = True
self.__lock.release()
while self.__running:
# 周遊每一隻螞蟻
for ant in self.ants:
# 搜尋一條路徑
ant.search_path()
# 與目前最優螞蟻比較
if ant.total_distance < self.best_ant.total_distance:
# 更新最優解
self.best_ant = copy.deepcopy(ant)
# 更新資訊素
self.__update_pheromone_gragh()
print(u"疊代次數:",self.iter,u"最優路徑所得總距離:",int(self.best_ant.total_distance))
# 連線
self.line(self.best_ant.path)
# 設定标題
self.title("TSP蟻群算法(i:随機初始 e:開始搜尋 s:停止搜尋 q:退出程式) 疊代次數: %d" % self.iter)
# 更新畫布
self.canvas.update()
self.iter += 1
# 更新資訊素
def __update_pheromone_gragh(self):
# 擷取每隻螞蟻在其路徑上留下的資訊素
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]
# 在路徑上的每兩個相鄰城市間留下資訊素,與路徑總距離反比
temp_pheromone[start][end] += Q / ant.total_distance
temp_pheromone[end][start] = temp_pheromone[start][end]
# 更新所有城市之間的資訊素,舊資訊素衰減加上新疊代資訊素
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]
# 主循環
def mainloop(self):
self.root.mainloop()
#----------- 程式的入口 -----------
if __name__ == '__main__':
print(u"""
--------------------------------------------------------
程式:蟻群算法解決簡單TSP問題
作者:_jiao
日期:2019-05-15
語言:Python
--------------------------------------------------------
""")
TSP(tkinter.Tk()).mainloop()
最後,該🐜問題疊代的最優路徑為:3419。
🆗 半年後發出來這麼簡單的,,,emm…僅供參考~
雖說是上半年看了幾個月的算法,找了好多篇文章,寫了篇論文,但還是感覺自己腦子一片空白😆
好了,不瞎扯了🤐