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###############################################################################
# Emir: EmiR: Evolutionary minimization forR #
# Copyright (C) 2021 Davide Pagano & Lorenzo Sostero #
# #
# This program is free software: you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as published by #
# the Free Software Foundation, either version 3 of the License, or #
# any later version. #
# #
# This program is distributed in the hope that it will be useful, but #
# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY #
# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details: <https://www.gnu.org/licenses/>. #
###############################################################################
#' Configuration object for the Simulated Annealing Algorithm
#'
#' Create a configuration object for the Simulated Annealing algorithm (SA). At minimum the number of iterations
#' (parameter `iterations`) and the number of particles (parameter `population_size`) have
#' to be provided.
#'
#' @param iterations maximum number of iterations.
#' @param population_size number of particles.
#' @param iterations_same_cost maximum number of consecutive iterations with the \emph{same}
#' (see the parameter `absolute_tol`) best cost before ending the minimization. If `NULL` the
#' minimization continues for the number of iterations specified by the parameter `iterations`.
#' Default is `NULL`.
#' @param absolute_tol absolute tolerance when comparing best costs from consecutive iterations.
#' If `NULL` the machine epsilon is used. Default is `NULL`.
#' @param T0 initial temperature. Default is `50`.
#' @param Ns number of iterations before changing velocity. Default is `3`.
#' @param Nt number of iterations before changing the temperature. Default is `3`.
#' @param c_step parameter involved in the velocity update. Default is `2`.
#' @param Rt scaling factor for the temperature. Default is `0.85`.
#' @param Wmin parameter involved in the generation of the starting point. Default is `0.25`.
#' @param Wmax parameter involved in the generation of the starting point. Default is `1.25`.
#' @return `config_sa` returns an object of class `SAConfig`.
#' @importFrom Rdpack reprompt
#' @references \insertRef{Kirkpatrick1983}{EmiR}
#' @export
#'
#' @examples
#' conf <- config_sa(iterations = 100, population_size = 50, iterations_same_cost = NULL,
#' absolute_tol = NULL, T0 = 50., Ns = 3., Nt = 3., c_step = 2., Rt = 0.85, Wmin = 0.25,
#' Wmax = 1.25)
#'
config_sa <- function(iterations,
population_size,
iterations_same_cost = NULL,
absolute_tol = NULL,
T0 = 50.,
Ns = 3.,
Nt = 3.,
c_step = 2.,
Rt = 0.85,
Wmin = 0.25,
Wmax = 1.25) {
p <- new("SAConfig")
commonOpt <- checkCommonConfigOptions(iterations, population_size, iterations_same_cost, absolute_tol)
p@iterations <- commonOpt$iterations
p@population_size <- commonOpt$population_size
p@iterations_same_cost <- commonOpt$iterations_same_cost
p@absolute_tol <- commonOpt$absolute_tol
p@T0 <- T0
p@Ns <- Ns
p@c_step <- c_step
p@Nt <- Nt
p@Rt <- Rt
p@Wmin <- Wmin
p@Wmax <- Wmax
return(p)
}
check_algo_options_sa <- function(p, ...) {
config_options <- list(...)
if (length(config_options) == 0) return(p)
for (i in 1:length(config_options)) {
if (names(config_options[i]) == "T0") {
p@T0 <- config_options[[i]]
} else if (names(config_options[i]) == "Ns") {
p@Ns <- config_options[[i]]
} else if (names(config_options[i]) == "c_step") {
p@c_step <- config_options[[i]]
} else if (names(config_options[i]) == "Nt") {
p@Nt <- config_options[[i]]
} else if (names(config_options[i]) == "Rt") {
p@Rt <- config_options[[i]]
} else if (names(config_options[i]) == "Wmin") {
p@Wmin <- config_options[[i]]
} else if (names(config_options[i]) == "Wmax") {
p@Wmax <- config_options[[i]]
} else {
stop(paste0("Unknown option '", names(config_options[i]), "' for algorithm SA."))
}
}
return(p)
}
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