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Copy pathHybrid of GA+SA+TS
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Hybrid of GA+SA+TS
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GAfitPara= function(pop,inf,n=dim(pop)[1]){
ft=data.frame(matrix(0,nrow=n,ncol=1))
colnames(ft)="Fitness"
cl=makeCluster(50)
registerDoParallel(cl)
result=foreach(k=1:n,.combine = rbind,.errorhandling = "pass",.export = c("GAfit","pop","hzd3","inf","use_lethal_temperature","lethal_temperature","lethal_temperature_month",
"infected_speci","susceptible_speci","total_pl","mortality_on","weather","temperature","wc",
"ew_res","ns_res","num_rows","num_cols","time_step","reproductive_rate","mortality_rate","mortality_time_lag",
"season_month_start", "season_month_end","start_time","end_time","natural_kernel_type","anthropogenic_kernel_type",
"use_anthropogenic_kernel", "percent_natural_dispersal","natural_distance_scale", "anthropogenic_distance_scale",
"natural_dir", "natural_kappa","anthropogenic_dir", "anthropogenic_kappa"),.packages = c("rgdal","raster","PoPS","rgeos"))%dopar%
{
vars=as.vector(unlist(pop[k,]))
return(GAfit(vars,inf))
}
stopCluster(cl)
ft=data.frame(result)
colnames(ft)="Fitness"
return(ft)
}
SAparFun= function(k){
#wt20=c(rep(4,15),rep(2,10),rep(0.5,16))
#wt50=c(rep(0.01,80),rep(1,15),rep(2,14))
ply50=hzd3[best_pop,]
ply20=hzd3[-best_pop,]
initial_selections=ply50
more_selections=ply20
nLocation=length(ply50)
newLocation=length(ply20)
nl1=round(nLocation*0.75)
nl2=round(nLocation*0.95)-round(nLocation*0.75)
nl3=nLocation-nl1-nl2
ne1=round(newLocation*0.75)
ne2=round(newLocation*0.95)-round(newLocation*0.75)
ne3=newLocation-ne1-ne2
p=c(1,3,9)
wt20=c(rep(p[3],ne3),rep(p[2],ne2),rep(p[1],ne1))
wt50=c(rep(p[1],nl1),rep(p[2],nl2),rep(p[3],nl3))
tl=seq(3,0,by=-0.3)
nl=length(tl)
number_iteration= 100
f1=SAfit(initial_selections,inf)
best=initial_selections
ply20$remove=0
ply50$remove=0
df=data.frame(matrix(0,ncol=1,nrow=length(tl)*number_iteration))
Ns=1
nbest=Ns
for (r in 1:length(tl)){
t=tl[r]
for (i in 1:number_iteration){
if (length(ply20)==0){break}
id50=sample(1:length(wt50),1,prob=wt50)
s50=ply50[id50,]
id20=sample(1:length(ply20),1,prob=wt20)
s20=ply20[id20,]
trt=rbind(ply50[-id50,],s20)
f2=SAfit(trt,inf)
dif=f2-f1
if (dif>80){
ply20$remove[id20]=ply20$remove[id20]+1
wt20=wt20[ply20$remove<10]
ply20=ply20[ply20$remove<10,]
}
if (f2<=f1){
ply50=rbind(ply50[-id50,],s20)
ply20=rbind(s50,ply20[-id20,])
f1=f2
best=ply50
nbest=Ns
}
if (dif<=80 & dif>0) {
prop=exp(1)^(-dif/t)
if (prop<1){
n=floor(prop*100)
if (n>0){
n2=100-n
chid=sample(1:2,1,prob = c(n,n2))
if (chid==1){
ply50=rbind(ply50[-id50,],s20)
ply20=rbind(s50,ply20[-id20,])
#contr=res
f1=f2
}
}
}
}
Ns=Ns+1
print(f1)
}
}
ids=best$ID
bestft=SAfit(best,inf)
re=list(bestft,ids)
return(re)
#return(best)
}
crossOverMute=function(N=150,inputVars=SAresult,psize=1000,sele=109){
cross_number=psize-dim(inputVars)[1]
n1=round(cross_number*0.5)
n2=cross_number-n1
varNew=data.frame(matrix(0,ncol=sele))
for (k in 1:n1){
ids=sample(1:dim(inputVars)[1],2,prob=dim(inputVars)[1]:1)
var2=inputVars[ids,]
ids=c(as.vector(unlist(var2[1,],var2[2,])))
ids2=unlist(unique(ids))
l=length(ids2)
ids2=ids2[order(ids2,decreasing = F)]
nvar=ids2[sample(1:l,sele)]
varNew[k,1:sele]=nvar
}
# mutation
n3=dim(inputVars)[1]
n4=floor(n2/n3)
for (i in 1:(n3-1)){
df=setdiff(c(1:N),as.vector(unlist(inputVars[i,1:sele])))
df=df[order(df,decreasing = F)]
nvar=as.vector(unlist(inputVars[i,1:sele]))
nvar2=nvar[order(nvar,decreasing = F)]
nmu=sample(1:3,1,prob=c(0.65,0.25,0.1))
nmu
for (pv in 1:n4){
muN=sample(df,nmu,prob=c(length(df):1))
muVar=sample(1:length(nvar2),nmu,prob=c(1:length(nvar2)))
nvar3=nvar2
nvar3[muVar]=muN
nvar3=data.frame(matrix(nvar3,nrow=1))
colnames(nvar3)=colnames(varNew)
varNew=rbind(varNew,nvar3)
}
}
df=setdiff(c(1:N),as.vector(unlist(inputVars[i,1:sele])))
df=df[order(df,decreasing = F)]
nvar=inputVars[n3,1:sele]
nvar=as.vector(unlist(inputVars[i,1:sele]))
nvar2=nvar[order(nvar,decreasing = F)]
n5=n2-(n3-1)*n4
for (pv in 1:n5){
muN=sample(df,1,prob=length(df):1)
muVar=sample(1:length(nvar2),1,prob=c(1:length(nvar2)))
nvar3=nvar2
nvar3[muVar]=muN
nvar3=data.frame(matrix(nvar3,nrow=1))
colnames(nvar3)=colnames(varNew)
varNew=rbind(varNew,nvar3)
}
colnames(inputVars)=colnames(varNew)
newVar=rbind(inputVars,varNew)
return(newVar)
}
inipop2=function(nvar=150,sele=109,psize=200){
id=1:nvar
pop=as.data.frame(matrix(0,nrow=psize,ncol=sele))
pop=as.matrix(pop)
n1=floor(sele*0.70)
n2=floor(sele*0.20)
n3=floor(sele*0.05)
n4=nvar-n1-n2-n3
wt=c(rep(4,n1),rep(3,n2),rep(2,n3),rep(1,n4))
for (i in 1:psize){
pop[i,]=as.numeric(sample(id,sele,prob=wt))
}
pop=as.matrix(pop)
pop[1,1:sele]=1:sele
return(pop)
}
library(foreach)
library(parallel)
library(doParallel)
pop=inipop(nvar=150,sele=109,psize=10000)
sele=109
N=150
nvar=150
psize=1000
library(tidyverse)
t11=proc.time()
for (i in 1:10){
for (c in 1:dim(pop)[1]){
elem=as.vector(unlist(pop[c,]))
pop[c,1:sele]=elem[order(elem)]
}
pop=pop[!duplicated(pop),]
ftPop=GAfitPara(pop,inf,n=dim(pop)[1])
best_pop=pop[which.min(ftPop$Fitness),1:sele]
best_pop=as.vector(unlist(best_pop))
pop=pop[order(ftPop$Fitness),]
best_pop=best_pop[order(best_pop)]
ft=min(ftPop$Fitness)
print(ft)
cl=makeCluster(60)
registerDoParallel(cl)
SAresult=foreach(k=1:60,.combine = rbind,.errorhandling = "pass",.export = c("ft","best_pop","SAparFun","ply20","ply50","SAfit","hzd3","inf","use_lethal_temperature","lethal_temperature","lethal_temperature_month",
"infected_speci","susceptible_speci","total_pl","mortality_on","weather","temperature","wc",
"ew_res","ns_res","num_rows","num_cols","time_step","reproductive_rate","mortality_rate","mortality_time_lag",
"season_month_start", "season_month_end","start_time","end_time","natural_kernel_type","anthropogenic_kernel_type",
"use_anthropogenic_kernel", "percent_natural_dispersal","natural_distance_scale", "anthropogenic_distance_scale",
"natural_dir", "natural_kappa","anthropogenic_dir", "anthropogenic_kappa"),.packages = c("rgdal","raster","PoPS","rgeos"))%dopar%
{
ftva=SAparFun(k)
#return(ftva[[1]])
if (ftva[[1]]<ft){
return(ftva[[2]])
}
}
stopCluster(cl)
SAresult=as.data.frame(SAresult)
if (n_SA!=0){
SAresult=SAresult[!duplicated(SAresult),]
colnames(SAresult)=colnames(pop)
}
n_SA=dim(SAresult)[1]
n_SA
SAresult=rbind(SAresult,pop[1:50,])
n_crossmute=10000
pop=crossOverMute(150,SAresult,n_crossmute,109)
}
proc.time()-t11