R/emoa.sms-emoa.R

In jakobbossek/ecr: Evolutionary Computing in R

#' @title
#' Implementation of the SMS-EMOA by Emmerich et al.
#'
#' @description
#' Pure R implementation of the SMS-EMOA. This algorithm belongs to the group
#' of indicator based multi-objective evolutionary algorithms. In each generation,
#' the SMS-EMOA selects two parents uniformly at, applies recombination and mutation
#' and finally selects the best subset of individuals among all subsets by maximizing
#' the Hypervolume indicator.
#'
#' @note
#' This helper function hides the regular \pkg{ecr} interface and offers a more
#' R like interface of this state of the art EMOA.
#'
#' @keywords optimize
#'
#' @references
#' Beume, N., Naujoks, B., Emmerich, M., SMS-EMOA: Multiobjective selection based
#' on dominated hypervolume, European Journal of Operational Research, Volume 181,
#' Issue 3, 16 September 2007, Pages 1653-1669.
#'
#' @template arg_optimization_task
#' @param n.population [\code{integer(1)}]\cr
#'   Population size. Default is \code{100}.
#' @param ref.point [\code{numeric}]\cr
#'   Reference point for the hypervolume computation. Default is (11, ..., 11)'
#'   with the corresponding dimension.
#' @template arg_parent_selector
#' @template arg_mutator
#' @template arg_recombinator
#' @template arg_max_iter
#' @template arg_max_evals
#' @template arg_max_time
#' @param ... [any]\cr
#'   Further arguments passed to \code{\link{setupECRControl}}.
#' @return [\code{ecr_smsemoa_result, ecr_multi_objective_result}]
#' @export
smsemoa = function(
  task,
  n.population = 100L,
  ref.point = NULL,
  parent.selector = setupSimpleSelector(),
  mutator = setupPolynomialMutator(eta = 25, p = 0.2),
  recombinator = setupSBXRecombinator(eta = 15, p = 0.7),
  max.iter = NULL,
  max.evals = NULL,
  max.time = NULL, ...) {

  if (isSmoofFunction(task)) {
    task = makeOptimizationTask(task)
  }
  assertClass(task, "ecr_optimization_task")

  # SMS-EMOA control object
  ctrl = setupECRControl(
    n.population = n.population,
    n.offspring = 1L,
    representation = "float",
    stopping.conditions = list(
      setupMaximumEvaluationsTerminator(max.evals),
      setupMaximumTimeTerminator(max.time),
      setupMaximumIterationsTerminator(max.iter)
    ),
    ...
  )
  ctrl = setupEvolutionaryOperators(
    ctrl,
    parent.selector = parent.selector,
    recombinator = recombinator,
    mutator = mutator,
    survival.selector = setupDominatedHypervolumeSelector()
  )

  ctrl$ref.point = ref.point
  if (is.null(ref.point)) {
    ctrl$ref.point = rep(11, task$n.objectives)
  }
  if (length(ctrl$ref.point) != task$n.objectives) {
    stopf("Reference point ref.point needs to have as many components as objectives.")
  }

  res = doTheEvolution(task, ctrl)
  res = BBmisc::addClasses(res, "ecr_smsemoa_result")
  return(res)
}