python蚁群算法 路径规划_使用python实现蚁群算法
此次使用python实现蚁群算法是仿照蚁群优化算法的JAVA实现中的蚁群算法实现方法，使用的也是其中的数据(此处为上传数据)，如需更深一步了解蚁群算法原理和具体实现过程，请参考蚁群优化算法的JAVA实现和蚁群算法java实现以及TSP问题蚁群算法求解。
# ants 类
import numpy as np
import random
import math
class ant:
Tour = 0
unvisitedcity = 0
tourlength = 0
citys = 0
count = 0
def RandomSelectCity(self,citycount):
ant.citys = citycount
ant.tourlength = 0
ant.Tour = np.zeros((citycount+1),int)
ant.unvisitedcity = np.ones((citycount))
for i in np.arange(citycount):
ant.Tour[i] = -1
firstcity = random.randint(0,citycount-1)
ant.unvisitedcity[firstcity] = 0 #0表示已经访问过
ant.Tour[0] = firstcity #起始城市
def SelectNextCity(self,index,tao,distance):
p = np.zeros((ant.citys)) #citys
x = 0
alpha = 1.0
beta = 2.0
sum = 0
currentcity = ant.Tour[index-1]
# print("curr",ant.Tour)
# print(ant.unvisitedcity)
for i in range(ant.citys):
# print(ant.unvisitedcity[i])
if ant.unvisitedcity[i] == 1:
sum += (math.pow(tao[currentcity][i], alpha) * math.pow(1.0/distance[currentcity][i], beta))
# 计算每个城市被选中的概率
for i in range(ant.citys):
if ant.unvisitedcity[i] == 0:
p[i] = 0.0
else:
p[i] = (math.pow(tao[currentcity][i], alpha) * math.pow(1.0/distance[currentcity][i], beta))/sum
selectp = random.random()
# 轮盘赌选择一个城市
sumselect = 0.0
selectcity = -1
for i in range(ant.citys):
sumselect += p[i]
# print("i",i,"sumselect",sumselect)
if sumselect >= selectp:
selectcity = i
break
if selectcity == -1:
print('')
ant.Tour[index] = selectcity
# print("selectcity",selectcity)
ant.unvisitedcity[selectcity] = 0
# print(ant.Tour)
# 计算蚂蚁获得的路径的长度
# distance 全局的距离矩阵信息
def CalTourLength(self,distance):
ant.Tour[ant.citys] = ant.Tour[0]
for i in range(ant.citys):
ant.tourlength += distance[int(ant.Tour[i])][int(ant.Tour[i+1])]
# ACO类
from ACO_implement.ACO_implement import ants
import numpy as np
import math
import sys
class ACO:
# 定义蚂蚁群
ants = []
antcount = 0 # 蚂蚁数量
distance = 0 #城市之间的距离
tao = 0 #信息素矩阵
citycount = 0 #城市数量
besttour = 0 #求解的最佳路径
bestlength = 0 #求解最优解的长度
def __init__(self,filename,antnum):
ACO.antcount = antnum
#读取数据
x = []
y = []
z = []
str = open(filename).readlines()
ACO.citycount = int(str[0])
for i in range(1,ACO.citycount+1):
strbuff = str[i].split()
x.append(int(strbuff[1]))
y.append(int(strbuff[2]))
# 计算距离矩阵，计算的是点之间的距离
ACO.distance = np.zeros((ACO.citycount,ACO.citycount))
for i in range(ACO.citycount):
for j in range(ACO.citycount):
ACO.distance[i][j] = math.sqrt(pow(x[i]-x[j],2)+pow(y[i]-y[j],2))
# 初始化信息矩阵
ACO.tao = np.zeros((ACO.citycount,ACO.citycount))
for i in range(ACO.citycount):
for j in range(ACO.citycount):
ACO.tao[i][j] = 0.1
ACO.bestlength = sys.maxsize #最优路径长度
ACO.besttour = np.zeros((ACO.citycount)) #最优路径
# 随机放置蚂蚁
# for i in range(ACO.antcount):
# ACO.ants.append(ants.ant())
# ACO.ants[i].RandomSelectCity(ACO.citycount)
"""
跟新信息素矩阵
"""
def UpdateTao(self):
rou = 0.5
# 信息素挥发
for i in range(ACO.citycount):
for j in range(ACO.citycount):
ACO.tao[i][j] = ACO.tao[i][j] *(1-rou)
# 信息素更新
for i in range(ACO.citycount):
for j in range(ACO.citycount):
ACO.tao[int(ACO.ants[i].Tour[j])][ACO.ants[i].Tour[j+1]] += 1.0/ACO.ants[i].tourlength
"""
ACO的运行过程
maxgen ACO的最多次循环次数
"""
def run(self,maxgen):
# 每只蚂蚁移动的过程
for runtimes in range(maxgen):
# 随机放置蚂蚁
for i in range(ACO.antcount):
ACO.ants.append(ants.ant())
ACO.ants[i].RandomSelectCity(ACO.citycount)
for j in range(1,ACO.citycount):
ACO.ants[i].SelectNextCity(j,ACO.tao,ACO.distance)
# 计算蚂蚁获得的路径的长度
ACO.ants[i].CalTourLength(ACO.distance)
if ACO.ants[i].tourlength < ACO.bestlength:
# 保留最优路径
ACO.bestlength = ACO.ants[i].tourlength
print("第",runtimes+1,"次,发现解",ACO.bestlength)
for j in range(0,ACO.citycount):
ACO.besttour[j] = ACO.ants[i].Tour[j]
ACO.UpdateTao(self)
for i in range(ACO.antcount):
ACO.ants[i].RandomSelectCity(ACO.citycount)
"""输出运行结果"""
def ReportResult(self):
print("最优路径长度是",ACO.bestlength)
print("最优路径为：",ACO.besttour+1)
#main类
from ACO_implement.ACO_implement import ACO
aco = ACO.ACO("ant_data",1000)
# aco.init("ant_data",1000)
#点数量为50，蚂蚁数量
aco.run(200)
aco.ReportResult()
转载自：https://blog.csdn.net/ZHIXINGHEYITUOPENG/article/details/80819727