## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(python.reticulate = FALSE)

## ----caRa, eval=F, echo=T-----------------------------------------------------
# library(caRamel)

## ----pbdMPI, eval=F, echo=T---------------------------------------------------
# library(pbdMPI)

## ----kursawe, eval=F, echo=T--------------------------------------------------
# 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))
# }

## ----kursawe_variable, eval=F, echo=T-----------------------------------------
# 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]

## ----kursawe_objectives, eval=F, echo=T---------------------------------------
# nobj <- 2 # number of objectives
# minmax <- c(FALSE, FALSE) # minimization for both functions

## ----kursawe_param, eval=F, echo=T--------------------------------------------
# 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) # accuracy for the convergence phase

## ----call_caRa, eval=F, echo=T------------------------------------------------
# results <-
#   caRamel(nobj,
#           nvar,
#           minmax,
#           bounds,
#           kursawe,
#           popsize,
#           archsize,
#           maxrun,
#           prec,
#           carallel = 1,
#           numcores = 20,
#           graph = FALSE,
#           verbose = FALSE)

## ----mpi_caRa, eval=F, echo=T-------------------------------------------------
# init()  # MPI functions from the pbdMPI package
# size <- comm.size()
# rank <- comm.rank()
# 
# results <- gather(optres, rank.dest = 0)  # gather all results on the main process
# 
# if (rank == 0) saveRDS(results, "Results.Rds")  # save all the results on disk
# 
# finalize()