#' @title
#' MMF15 Function
#'
#' @description
#' Test problem from the set of "multi-modal multi-objective functions" as for
#' instance used in the CEC2019 competition.
#'
#' @param dimensions [\code{integer(1)}]\cr
#' Number of decision variables.
#' @param n.objectives [\code{integer(1)}]\cr
#' Number of objectives.
#' @param np [\code{integer}(1)]\cr
#' Number of global Pareto sets. In the CEC2019 competition, the organizers used
#' \code{np = 2L}.
#'
#' @references
#' Caitong Yue, Boyang Qu, Kunjie Yu, Jing Liang, and Xiaodong Li, "A novel
#' scalable test problem suite for multi-modal multi-objective optimization," in
#' Swarm and Evolutionary Computation, Volume 48, August 2019, pp. 62–71, Elsevier.
#' @return [\code{smoof_multi_objective_function}]
#' Returns an instance of the MMF15 function as a \code{smoof_multi_objective_function} object.
#'
#' @export
makeMMF15Function = function(dimensions, n.objectives, np = 2L) {
checkmate::assertInt(n.objectives, lower = 2L)
checkmate::assertInt(dimensions, lower = n.objectives)
checkmate::assertInt(x = np, lower = 1L)
force(np)
# Renaming var here to stick to the notation in the paper
M = n.objectives
force(M)
# C implementation
fn = function(x) {
checkNumericInput(x, dimensions)
mof_cec2019_mmf15(x = x, M = M, np = np)
}
makeMultiObjectiveFunction(
name = "MMF15 function",
id = sprintf("MMF15-%id-%io", dimensions, n.objectives),
description = "MMF15 function",
fn = fn,
par.set = ParamHelpers::makeNumericParamSet(
len = dimensions,
id = "x",
lower = rep(0, dimensions),
upper = rep(1, dimensions),
vector = TRUE
),
minimize = rep(TRUE, n.objectives),
n.objectives = n.objectives
)
}
class(makeMMF15Function) = c("function", "smoof_generator")
attr(makeMMF15Function, "name") = c("MMF15")
attr(makeMMF15Function, "type") = c("multi-objective")
attr(makeMMF15Function, "tags") = c("multi-objective")
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