Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
## ----caRa---------------------------------------------------------------------
library(caRamel)
## ----kursawe------------------------------------------------------------------
kursawe <- function(i) {
k1 <- -10 * exp(-0.2 * sqrt(x[i,1] ^ 2 + x[i,2] ^ 2)) - 10 * exp(-0.2 * sqrt(x[i,2] ^2 + x[i,3] ^ 2))
k2 <- abs(x[i,1]) ^ 0.8 + 5 * sin(x[i,1] ^ 3) + abs(x[i,2]) ^ 0.8 + 5 * sin(x[i,2] ^3) + abs(x[i,3]) ^ 0.8 + 5 * sin(x[i,3] ^ 3)
return(c(k1, k2))
}
## ----parameters---------------------------------------------------------------
nvar <- 3 # number of variables
bounds <- matrix(data = 1, nrow = nvar, ncol = 2) # upper and lower bounds
bounds[, 1] <- -5 * bounds[, 1]
bounds[, 2] <- 5 * bounds[, 2]
nobj <- 2 # number of objectives
minmax <- c(FALSE, FALSE) # minimization for both objectives
popsize <- 100 # size of the genetic population
archsize <- 100 # size of the archive for the Pareto front
maxrun <- 1000 # maximum number of calls
prec <- matrix(1.e-3, nrow = 1, ncol = nobj) # convergence criteria
## ----multi--------------------------------------------------------------------
nrepeat <- 3 # number of calls to caRamel
concat_results_objectives <- NULL # save results for all the calls
concat_results_parameters <- NULL
for (i in seq(nrepeat)) {
optres <- caRamel(nobj,
nvar,
minmax,
bounds,
kursawe,
popsize,
archsize,
maxrun,
prec,
carallel = 0,
graph = FALSE,
verbose = FALSE)
concat_results_objectives <- rbind(concat_results_objectives,
optres$objectives)
concat_results_parameters <- rbind(concat_results_parameters,
optres$parameters)
}
## ----merge--------------------------------------------------------------------
results_objectives <- concat_results_objectives
results_objectives[, !minmax] <- -results_objectives[, !minmax] # important !
is_pareto <- pareto(results_objectives) # mask
global_results_objectives <- concat_results_objectives[is_pareto, ] # front from the three previous fronts
global_results_parameters <- concat_results_parameters[is_pareto, ]
## ----plot---------------------------------------------------------------------
plot(concat_results_objectives[, 1], concat_results_objectives[, 2],
main = "Kursawe Pareto fronts",
xlab = "Objective #1", ylab = "Objective #2")
points(global_results_objectives[, 1], global_results_objectives[, 2],
col = "red", pch = "*")
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