#' @title
#' Price Function N. 1
#'
#' @description
#' Second function by Price. The implementation is based on the definition
#' \deqn{f(\mathbf{x}) = (|\mathbf{x}_1| - 5)^2 + (|\mathbf{x}_2 - 5)^2}
#' subject to \eqn{\mathbf{x}_i \in [-500, 500], i = 1, 2}.
#'
#' @return
#' An object of class \code{SingleObjectiveFunction}, representing the Price N. 1 Function.
#'
#' @references W. L. Price, A Controlled Random Search Procedure for Global
#' Optimization, Computer journal, vol. 20, no. 4, pp. 367-370, 1977.
#'
#' @seealso \code{\link{makePriceN2Function}}, \code{\link{makePriceN4Function}}
#'
#' @template ret_smoof_single
#' @export
makePriceN1Function = function() {
makeSingleObjectiveFunction(
name = "Price Function N. 1",
id = "price01_2d",
fn = function(x) {
checkNumericInput(x, 2L)
sum((abs(x) - 5)^2)
},
par.set = ParamHelpers::makeNumericParamSet(
len = 2L,
id = "x",
lower = c(-500, -500),
upper = c(500, 500),
vector = TRUE
),
tags = attr(makePriceN1Function, "tags"),
global.opt.params = matrix(
c(5, 5,
-5, 5,
5, -5,
-5, 5),
ncol = 2L, byrow = TRUE),
global.opt.value = 0
)
}
class(makePriceN1Function) = c("function", "smoof_generator")
attr(makePriceN1Function, "name") = c("Price N. 1")
attr(makePriceN1Function, "type") = c("single-objective")
attr(makePriceN1Function, "tags") = c("single-objective", "continuous", "non-differentiable", "separable", "non-scalable", "multimodal")
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