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Copy pathSimulated Annealing (SA) + Tabu Search (TS)
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Simulated Annealing (SA) + Tabu Search (TS)
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setwd("C:/Users/wliang5/Desktop/Optimi_test/smAOI")
hzd3=readOGR(".","RankBigger")
inf=raster(infected_files)
#### Define Fitness function ####
# trt is spatialpolygons covering the treatment area, inf is the infected raster
SAfit= function(trt,inf){
trt=trt
trt=gUnionCascaded(trt)
toRa=rasterize(trt,inf,field=1,background=0)
trt=list(as.matrix(toRa))
df=data.frame(matrix(0,nrow=20,ncol=1))
for (j in 1:20){
random_seed= j*1000+50
data <- pops_model(random_seed = random_seed,
use_lethal_temperature = use_lethal_temperature,
lethal_temperature = lethal_temperature,
lethal_temperature_month = lethal_temperature_month,
infected = infected_speci[[1]],
susceptible = susceptible_speci[[1]],
total_plants = total_pl[[1]],
mortality_on = mortality_on,
mortality_tracker = infected_speci[[1]]*0,
mortality = infected_speci[[1]]*0,
treatment_maps = trt,
treatment_dates = c("2019-03-01"),
pesticide_duration=c(0),
resistant = infected_speci[[1]]*0,
weather = weather,
temperature = temperature,
weather_coefficient = wc,
ew_res = ew_res, ns_res = ns_res, num_rows = num_rows, num_cols = num_cols,
time_step = time_step, reproductive_rate = reproductive_rate[[2]],
mortality_rate = mortality_rate, mortality_time_lag = mortality_time_lag,
season_month_start = season_month_start, season_month_end = season_month_end,
start_date = start_time, end_date = end_time,
treatment_method = "all infected",
natural_kernel_type = natural_kernel_type[[1]], anthropogenic_kernel_type = anthropogenic_kernel_type[[1]],
use_anthropogenic_kernel = use_anthropogenic_kernel, percent_natural_dispersal = percent_natural_dispersal[[1]],
natural_distance_scale = natural_distance_scale[[2]], anthropogenic_distance_scale = anthropogenic_distance_scale[[1]],
natural_dir = natural_dir[[1]], natural_kappa = natural_kappa[[1]],
anthropogenic_dir = anthropogenic_dir[[1]], anthropogenic_kappa = anthropogenic_kappa[[1]],output_frequency = "year")
area=data$area_infected
area=area/10000
df[j,1]=area
}
mean=mean(df[1:20,1])
return(mean)
}
# test code
SAfit(hzd3, inf)
######## SA function for single run ########
## initial_selections and more_selections are initial selected locations and more new locations can be considered, respectively.
SA= function(initial_selections,more_selections,number_iteration=100){
# set cooling temperatures, values can be changed,
# larger values give more flexbility and can lead to better results with enough iterations
# important parameters
tl=seq(4,0,by=-0.4) # cooling temperatures
nl=length(tl)
f1=SAfit(initial_selections,inf)
ply50=initial_selections
ply20=more_selections
best=initial_selections
ply50$ID=1:length(ply50)
ply20$ID=(1+length(ply50)):(length(ply50)+length(ply20))
ply20$remove=0
ply50$remove=0
nLocation=length(ply50)
newLocation=length(ply20)
nl1=round(nLocation*0.5)
nl2=round(nLocation*0.75)-round(nLocation*0.5)
nl3=nLocation-nl1-nl2
ne1=round(newLocation*0.5)
ne2=round(newLocation*0.75)-round(newLocation*0.5)
ne3=newLocation-ne1-ne2
# set weights for picking up old location to be replaced with new location,
# important parameters
p=c(1,4,9)
wt20=c(rep(p[3],ne1),rep(p[2],ne2),rep(p[1],ne3))
wt50=c(rep(p[1],nl1),rep(p[2],nl2),rep(p[3],nl3))
df=data.frame(matrix(0,ncol=1,nrow=length(tl)*number_iteration))
Ns=0
for (r in 1:length(tl)){
t=tl[r]
for (i in 1:number_iteration){
if (length(ply20)==0){break}
id50=sample(1:length(wt50),1,prob=wt50)
s50=ply50[id50,]
id20=sample(1:length(ply20),1,prob=wt20)
s20=ply20[id20,]
trt=rbind(ply50[-id50,],s20)
f2=SAfit(trt,inf)
dif=f2-f1
if (dif>60){
ply20$remove[id20]=ply20$remove[id20]+1
wt20=wt20[ply20$remove<10]
ply20=ply20[ply20$remove<10,]
}
if (f2<=f1){
ply50=rbind(ply50[-id50,],s20)
ply20=rbind(s50,ply20[-id20,])
f1=f2
best=ply50
}
if (dif<=60 & dif>0) {
prop=exp(1)^(-dif/t)
if (prop<1){
n=floor(prop*100)
if (n>0){
n2=100-n
chid=sample(1:2,1,prob = c(n,n2))
if (chid==1){
ply50=rbind(ply50[-id50,],s20)
ply20=rbind(s50,ply20[-id20,])
#contr=res
f1=f2
}
}
}
}
Ns=Ns+1
df[Ns,1]=f1
print(f1)
}
}
ftb=SAfit(best,inf)
results=list(ftb,best)
return (results)
}
SArun=SA(ply50,ply20,200)
###### SA functions for parallel using foreach #######
SAparFun= function(k){
nLocation=length(ply50)
newLocation=length(ply20)
nl1=round(nLocation*0.5)
nl2=round(nLocation*0.75)-round(nLocation*0.5)
nl3=nLocation-nl1-nl2
ne1=round(newLocation*0.5)
ne2=round(newLocation*0.75)-round(newLocation*0.5)
ne3=newLocation-ne1-ne2
p=c(1,4,9)
wt20=c(rep(p[3],ne1),rep(p[2],ne2),rep(p[1],ne3))
wt50=c(rep(p[1],nl1),rep(p[2],nl2),rep(p[3],nl3))
tl=seq(4,0,by=-0.4)
nl=length(tl)
number_iteration= 2000
f1=SAfit(initial_selections,inf)
ply50=initial_selections
ply20=more_selections
best=initial_selections
ply50$ID=1:length(ply50)
ply20$ID=(1+length(ply50)):(length(ply50)+length(ply20))
ply20$remove=0
ply50$remove=0
df=data.frame(matrix(0,ncol=1,nrow=length(tl)*number_iteration))
Ns=0
for (r in 1:length(tl)){
t=tl[r]
for (i in 1:number_iteration){
if (length(ply20)==0){break}
id50=sample(1:length(wt50),1,prob=wt50)
s50=ply50[id50,]
id20=sample(1:length(ply20),1,prob=wt20)
s20=ply20[id20,]
trt=rbind(ply50[-id50,],s20)
f2=SAfit(trt,inf)
dif=f2-f1
if (dif>60){
#ply20=ply20[-id20,]
#wt20=wt20[-id20]
ply20$remove[id20]=ply20$remove[id20]+1
#wt20[id20]=wt20[id20]/2
wt20=wt20[ply20$remove<10]
ply20=ply20[ply20$remove<10,]
}
if (f2<=f1){
ply50=rbind(ply50[-id50,],s20)
ply20=rbind(s50,ply20[-id20,])
f1=f2
best=ply50
}
if (dif<=60 & dif>0) {
prop=exp(1)^(-dif/t)
if (prop<1){
n=floor(prop*100)
if (n>0){
n2=100-n
chid=sample(1:2,1,prob = c(n,n2))
if (chid==1){
ply50=rbind(ply50[-id50,],s20)
ply20=rbind(s50,ply20[-id20,])
#contr=res
f1=f2
}
}
}
}
}
}
bestft=SAfit(best,inf)
return(bestft)
#return(best$ID)
#return(best)
}
## Run SA in parallel ##
library(foreach)
library(parallel)
library(doParallel)
t1=proc.time()
cl=makeCluster(30)
registerDoParallel(cl)
result=foreach(k=1:30,.combine = rbind,.errorhandling = "pass",.export = c("SAparFun","ply20","best","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)
#if (ftva[[1]]<1490){
# re=ftva[[2]]
#return(re)
#} else {return(hzd3[526,])}
#return(ftva[[1]])
return(ftva)
}
stopCluster(cl)
proc.time()-t1
dim(result)
summary(result)