R语言-GA算法脚本

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#粗糙地计算总路程上界 UpperBound = function(disM) {     mx = apply(disM,1,max)  #两城市间最长距离     return( sum(mx) )       #最长旅行路径 }   #生成随机染色体 rndDNA = function(n) {     return( seq(1,n,1) )  #随机生成n条旅行路径 }   #对一个解计算总路程的距离 calcScores = function(dna,disM) {     n = length(dna)         #城市总数     tmp = cbind(dna[1:n],c(dna[2:n],dna[1])) #生成起始点城市     len = apply(tmp,1,function(x) disM[x[1],x[2]])     len = sum(len)     return(len)      #返回此条旅行路径总距离 }   #根据每条染色体的分数计算权重，并以此抽样 roller = function(scores,k) {     scores = max(scores)-scores+1     props = scores/sum(scores)     N = length(scores)     mxind = which.max(scores)#保留最优染色体     ans = sample((1:N)[-mxind],k-1,replace = F,prob = props[-mxind])     return(c(mxind,ans)) }   #种群中的繁殖过程 crossEvolve = function(i,nGroup,crossGroup,prop) {     a = nGroup[i,]     b = crossGroup[i,]     n = length(a)     m = max(1,trunc(n*prop))       tmpa = a       st = sample(1:n,1)     ind = st:(st+m)      #indication 指示、索引     if (st+m>n)     {         bind = which(ind>n)         ind[bind] = ind[bind] %% n + 1     }     cross = intersect(b,a[ind])     tmpa[ind] = cross     return(tmpa) }   #染色体的自我变异 selfVariation = function(dna,prop) {     n = length(dna)     pos = which(runif(n)<prop)     if (length(pos)==0)         return(dna)     pos = sample(pos,1)     newind = sample((1:n)[-pos],1)       if (pos>newind)     {         tmp = dna[newind:n]         tmp = tmp[-(pos-newind+1)]         dna[newind] = dna[pos]         dna[(newind+1):n] = tmp     }     else     {         tmp = dna[1:newind]         tmp = tmp[-pos]         dna[newind] = dna[pos]         dna[1:(newind-1)] = tmp     }     return(dna) }   #以某条染色体代表的解做图 drawIt = function(dots,dna,xlab=NULL,ylab=NULL,main=NULL,sub=NULL,col=NULL) { #win.graph(width=800, height=800, pointsize=12)       x = dots[[1]]     y = dots[[2]]       z = dots[[4]]       cols=dots[[5]]+10     if (is.null(main))     {         scores = calcScores(dna,dots[[3]]) #画地图  plot(border,border="#BBFFFF",col="#FF7F00",ylim = c(18, 54), panel.first = grid(),       main=paste("总路程",scores,"公里"),xlab="经度",ylab="纬度",sub=paste('优化结束-第',sub,'代')); #增加省会城市坐标点            points(x,y,pch=19,col=cols)            text(x,y,z,pos=1,cex=0.5)       n = length(dna)     for (i in 1:(n-1)){        Sys.sleep(0.3)       lines(x[dna[i:(i+1)]],y[dna[i:(i+1)]],col="#00FFFF",lwd=3)}     lines(x[dna[c(n,1)]],y[dna[c(n,1)]],col="#00FFFF",lwd=3)       }     else{   #画地图 plot(border,border="#BBFFFF",col="#FF7F00",ylim = c(18, 54),main=paste("总路程",main,"公里"),      xlab="经度",ylab="纬度",sub=paste('第',sub,'代')) points(x,y,pch=19,col=cols) if(sub==1)Sys.sleep(5) else{         n = length(dna)     for (i in 1:(n-1))        lines(x[dna[i:(i+1)]],y[dna[i:(i+1)]],col="#00FFFF",lwd=3)     lines(x[dna[c(n,1)]],y[dna[c(n,1)]],col="#00FFFF",lwd=3) }}}     #Genetic Algorithm for Traveller Salesman Problem GA4TSP = function(dots,initDNA=NULL,N,cp,vp,maxIter,maxStay,maxElite,drawing) {     disM = dots[[3]]     n = nrow(disM)     if (N %%2 >0)         N = N+1     Group = t(sapply(rep(n,N),rndDNA))             if (!is.null(initDNA)) Group[1,]=initDNA               maxL = UpperBound(disM)     stopFlag = FALSE     iterCount = 1     stayCount = 0     allBest = maxL     eliteBest = maxL     elite = mat.or.vec(maxElite,n)     elitecount = 0     eracount = 0       LastEra = maxL     outputRecorder = NULL     GenerationRecorder = NULL     showScore=FALSE     eliteInto=FALSE     #初始化结束           while (!stopFlag)     {         cat('Generation:',iterCount,'Era:',eracount,'Elite:',elitecount)         scores = apply(Group,1,calcScores,disM)         bestScore = min(scores)         mind = which.min(scores)         bestDNA = Group[mind,]#记录最佳染色体           #更新时代、精英的信息         if (bestScore<eliteBest)         {             stayCount = 0             eliteBest = bestScore             eliteDNA = bestDNA             if (eliteBest<allBest)             {                 allBest = eliteBest                 allDNA = eliteDNA                 outputRecorder = rbind(outputRecorder,allDNA)                 GenerationRecorder = c(GenerationRecorder,iterCount)                 if (drawing)                     drawIt(dots,allDNA,main=as.character(allBest),sub=iterCount)             }         }         else             stayCount = stayCount+1           if (stayCount == maxStay)         {             stayCount = 0             eliteBest = maxL             elitecount = elitecount+1             elite[elitecount,] = eliteDNA             scores = apply(Group,1,calcScores,disM)             a = which(scores == min(scores))             nind = sample((1:N)[-a],length(a))             Group[a,] = Group[nind,]             eliteInto =TRUE             scores = apply(Group,1,calcScores,disM)             bestScore = min(scores)             mind = which.min(scores)             bestDNA = Group[mind,]         }           if (elitecount==maxElite)         {             Group[1:elitecount,]=elite             elite = mat.or.vec(maxElite,n)             elitecount = 0             stayCount = 0             eliteBest = maxL             eracount = eracount+1             maxStay = maxStay + 20             showScore=TRUE             scores = apply(Group,1,calcScores,disM)             bestScore = min(scores)             mind = which.min(scores)             bestDNA = Group[mind,]         }                   #对种群计算分数，产生繁殖与变异         succind = roller(scores,N/2)          nGroup = Group[succind,]          #取最优和一半高权重优秀基因         crossind = sample(succind,N/2)         crossGroup = Group[crossind,]     #取最优和一半高权重优秀基因,打乱顺序         crossans = t(sapply(1:(N/2),crossEvolve,nGroup,crossGroup,cp))         crossGroup = rbind(nGroup,crossans)           Group = t(apply(crossGroup,1,selfVariation,vp))         if (eliteInto)             eliteInto = FALSE         else             Group[1,] = bestDNA             stopFlag = (iterCount>=maxIter)        iterCount = iterCount+1         cat(' Best:',bestScore,'All:',allBest,'Stay:',paste(stayCount,'/',maxStay,sep=''),'\n')         if (showScore)         {             scores = apply(Group,1,calcScores,disM)             show(scores[1:(N/2)])             show(scores[(N/2+1):(N)])             #Sys.sleep(1)             showScore=FALSE         }     }     if (drawing)         drawIt(dots,allDNA,sub=maxIter)  return(list(DNA = outputRecorder,Generation=GenerationRecorder)) }     #生成初始染色体 CreatDNA = function(data,i) { hc=hclust(dist(data[,1:2])) data\(col</code><code class="python keyword">=</code><code class="python plain">cutree(hc,&nbsp;k&nbsp;</code><code class="python keyword">=</code>&nbsp;<code class="python plain">i)&nbsp;</code><code class="python comments">#k&nbsp;=&nbsp;1&nbsp;is&nbsp;trivial</code></div><div class="line number250 index249 alt1"><code class="python plain">INITDNA</code><code class="python keyword">=</code><code class="python plain">data[order(data\)col),] rownames(INITDNA)=NULL     return(INITDNA)   }

反馈与建议

• Author：商丰瑞
• 邮箱：shangfengrui@gmail.com
• date: 2015年10月26日

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