无人车与无人机蚁群动态揽收.zip

# -*- coding: utf-8 -*- """ Spyder Editor This temporary script file is located here: C:\Users\潘sir\.spyder2\.temp.py """ import numpy as np np.set_printoptions(threshold='nan') import math import matplotlib.pyplot as plt import dongtai import datetime numant = 10 #蚂蚁个数 w_weight = 0#无人车初始重量 alpha = 1 #信息素重要程度因子 #beta = 5 #启发函数重要程度因子 beta = 2 rho = 0.1 #信息素的挥发速度 Q = 1 speed_car=1 #无人车速度 speed_drone=2#无人机速度 itermax = 500 lengthaver = np.zeros(itermax) #各代路径的平均长度 lengthbest = np.zeros(itermax) #各代及其之前遇到的最佳路径长度 def getdistmat(coordinates): num = coordinates.shape[0] distmat = np.zeros((num,num)) for i in range(num): for j in range(i,num): distmat[i][j] = distmat[j][i]=np.linalg.norm(coordinates[i]-coordinates[j])#norm 两点距离函数 return distmat def Ant_algorithm(distmat,weight,numcity,maxcarweight):##蚁群算法进行初始无人车路径规划 pheromonetable = np.ones((numcity,numcity)) # 信息素矩阵 ones返回初始值为1的矩阵 pathtable = np.zeros((numant,numcity*2-1)).astype(int) #路径记录表 pathbest = np.zeros((itermax,numcity*2-1)) # 各代及其之前遇到的最佳路径长度 etatable = 1.0/(distmat+np.diag([1e10]*numcity)) #启发函数矩阵，表示蚂蚁从城市i转移到矩阵j的期望程度 diag 形成对角矩阵 iter = 0 for i in range(numant): for j in range(numcity*2-1): pathtable[i][j] = -1 while iter < itermax: ''' # 随机产生各个蚂蚁的起点城市 if numant <= numcity:#城市数比蚂蚁数多 pathtable[:,0] = np.random.permutation(range(0,numcity))[:numant]#产生numant个数，放在第0列上 else: #蚂蚁数比城市数多，需要补足 pathtable[:numcity,0] = np.random.permutation(range(0,numcity))[:] pathtable[numcity:,0] = np.random.permutation(range(0,numcity))[:numant-numcity] ''' pathtable[:,0] = 0 length = np.zeros(numant) #计算各个蚂蚁的路径距离 for i in range(numant): w_weight = 0 visiting = pathtable[i,0] # 当前所在的城市 #visited = set() #已访问过的城市，防止重复 #visited.add(visiting) #增加元素 unvisited = set(range(numcity))#未访问的城市 unvisited.remove(visiting) #删除元素 j=1 while(len(unvisited) != 0): listunvisited = list(unvisited) flag=0 probtrans = np.zeros(len(listunvisited)) can_unvisited=[0]*len(listunvisited) for l in range (len(listunvisited)): if(w_weight + weight[listunvisited[l]] <= maxcarweight):#判断是否超出无人车最大载重，若没有，则加入无人车 can_unvisited[l] = listunvisited[l] for k in range(len(can_unvisited)): if(can_unvisited[k]>0):#计算信息素 probtrans[k] = np.power(pheromonetable[visiting][listunvisited[k]],alpha)\ *np.power(etatable[visiting][listunvisited[k]],beta) flag = 1 if(flag == 1): cumsumprobtrans = (probtrans/sum(probtrans)).cumsum()#使最大概率变为1，轮盘必会找到城市 cumsumprobtrans -= np.random.rand() for cc in range(len(cumsumprobtrans)): if(cumsumprobtrans[cc]>0): kw=cc break k = listunvisited[[kw][0]] pathtable[i,j] = k w_weight = w_weight + weight[k] unvisited.remove(k) length[i] += distmat[visiting][k] visiting = k j += 1 else: length[i] += distmat[visiting][0] w_weight = 0 visiting = 0 pathtable[i,j] = 0 j += 1 length[i] += distmat[visiting][0] pathtable[i,j] = 0 #添加结束点0 # 包含所有蚂蚁的一个迭代结束后，统计本次迭代的若干统计参数 lengthaver[iter] = length.mean() if iter == 0:#寻找总路程最短 lengthbest[iter] = length.min() pathbest[iter] = pathtable[length.argmin()].copy()#argmin 最小值下标 else: if length.min() > lengthbest[iter-1]: lengthbest[iter] = lengthbest[iter-1] pathbest[iter] = pathbest[iter-1].copy() else: lengthbest[iter] = length.min() pathbest[iter] = pathtable[length.argmin()].copy() # 更新信息素 changepheromonetable = np.zeros((numcity,numcity)) for i in range(numant): #nnnum=0 for j in range(numcity*2-2): if((pathtable[i,j] != -1) and (pathtable[i,j] != -1) ): #if(distmat[pathtable[i,j]][pathtable[i,j+1]] == 0): #continue changepheromonetable[pathtable[i,j]][pathtable[i,j+1]] += Q/distmat[pathtable[i,j]][pathtable[i,j+1]] #changepheromonetable[pathtable[i,nnnum]][pathtable[i,0]] += Q/distmat[pathtable[i,nnnum]][pathtable[i,0]] pheromonetable = (1-rho)*pheromonetable + changepheromonetable iter += 1 #迭代次数指示器+1 #print "outyiqun" return pathbest def print_path(pathbest,numcity):#建立多维数组，每行代表一条路径 bestpath = pathbest[-1] num = 0 m=1 x = 0 y = 1 flag = 1 for j in range(len(bestpath)): if(bestpath[j] == 0): num += 1 finalpathtable = np.zeros((num-1,numcity+1)).astype(int) for i in range(num-1): for j in range(numcity+1): finalpathtable[i][j] = -1 finalpathtable[:,0] = 0 while(flag == 1): if(bestpath[m] == 0 and bestpath[m+1] == -1): break if(bestpath[m] == 0): finalpathtable[x][y] = 0 x += 1 y = 1 else: finalpathtable[x][y] = bestpath[m] y += 1 m += 1 finalpathtable[x][y] = 0 return finalpathtable def outqingjian(path1,weight,numcity,coordinates,weightflag,drone_weight): jilubiao = [[[0 for a in range(2)] for b in range(len(path1[0]))] for c in range(len(path1))] m=[] zonglu=[] zhongwe=[] for i in range (len(weight)): if(weight[i] < drone_weight and weight[i] > 0): weightflag[i] = 0 else: weightflag[i] = 1 weightflag[0] = 1 for i in range (len(path1)): for k in path1[i]:#将所有点加入有个分组，和所有重件点加入一个分组 if(k!=-1): zonglu.append(coordinates[k]) if(weightflag[k] == 1 and k !=-1): zhongwe.append(coordinates[k]) for i in range (len(path1)): count=0 l=[] for k in path1[i]: if(weightflag[k] == 1 and k !=-1): l.append(coordinates[k]) jilubiao[i][count][0]=k jilubiao[i][count][1]=1 else: jilubiao[i][count][0]=k jilubiao[i][count][1]=0 count+=1 m.append(l) #print "outqingjian" return m,weightflag,jilubiao,zonglu,zhongwe def outqingjian_all(path1,weight,numcity,coo