The package randomsearch optimizes a given smoof function. Use makeSingleObjectiveFunction() to wrap any function into a smoof function. It can make use of parallel resources through prallelMap. randomsearch has three internal operating modes:

• fast.parallel is the default and used if only max.evals is defined as termination criterion. max.execbudget and target.fun.value have to be NULL.
• slow.parallel is used if max.execbudget or target.fun.value have been set and parallelStart has been called without defined level or with level = randomsearch.feval. In this mode par.jobs (defaults to parallelGetOptions()$settings$cpus) instances are started. Each instance conducts a randomsearch until the termination criterion is met. The instances communicate using a shared folder (par.dir = ~/.randomsearch/).
• normal is used if no parallel mode is started and if max.execbudget or target.fun.value have been set. Note, that this introduces an overhead to fast.parallel even without parallelization because the termination criterion is checked after each evaluation.

Usage

obj.fun = makeSingleObjectiveFunction(
  fn = function(x) x[1]^2 + sin(x[2]),
  par.set = makeNumericParamSet(len = 2, lower = -1, upper = 2),
  minimize = TRUE
)
res = randomsearch(obj.fun, max.evals = 30)
rs = summary(res)
rs$best.x

## $x
## [1] -0.2617368 -0.8738214

rs$best.y

##          y 
## -0.6982813

tail(as.data.frame(res))

##            x1         x2          y dob eol exec.time
## 25 -0.2020821  1.5734831 1.04083356  25  NA         0
## 26 -0.8625065  0.3266002 1.06474229  26  NA         0
## 27  1.3967745 -0.6343022 1.35836350  27  NA         0
## 28  0.6828440 -0.3804058 0.09497854  28  NA         0
## 29 -0.6174050  1.2599236 1.33325597  29  NA         0
## 30  1.6851361  0.1233883 2.96275907  30  NA         0

Parallel Usage

Note: For Windows use parallelStartSocket().

obj.fun = makeSingleObjectiveFunction(
  fn = function(x) {
    Sys.sleep(runif(1))
    x[1]^2 + sin(x[2])
  },
  par.set = makeNumericParamSet(len = 2, lower = -1, upper = 2),
  minimize = TRUE
)
parallelMap::parallelStartMulticore(cpus = 2, level = "randomsearch.feval")
res = randomsearch(obj.fun, max.execbudget = 2, max.evals = 1000)
summary(res)

Multi-objective optimization

obj.fun = makeMultiObjectiveFunction(
  fn = function(x) c(x[1]^2 + sin(x[2]), cos(x[1])),
  par.set = makeNumericParamSet(len = 2, lower = -1, upper = 2),
  minimize = c(TRUE, TRUE)
)
res = randomsearch(obj.fun, max.evals = 30)
summary(res)

## Randomsearch Result: 
## Multiobjective Search Pareto Front 
##           y_1         y_2         x1         x2
## 4  -0.1517177  0.91064673 0.42594972 -0.3396433
## 6  -0.4762416  0.97319255 0.23206933 -0.5587159
## 7  -0.6152440  0.99846616 0.05539373 -0.6665937
## 9   0.4376802  0.42616485 1.13054720 -0.9981257
## 13  0.8074995  0.39769890 1.16178882 -0.5731171
## 14  1.1552992  0.22204704 1.34688290 -0.7192150
## 18  2.2268508  0.18666793 1.38302696  0.3194951
## 24  2.2964342 -0.08849494 1.65940718 -0.4748420
## 25  3.1651082 -0.23294323 1.80589941 -0.0963133
## 27  4.8156555 -0.37470133 1.95487094  1.6791533