**caRamel** is a multiobjective evolutionary algorithm
combining the MEAS algorithm and the NGSA-II algorithm.

Download the package from CRAN or GitHub and then install and load it.

`library(caRamel)`

This example will use the **pbdMPI** package in order to
use several processes of caRamel. Download the package from CRAN and
then install and load it. Make also sure that you have an MPI
distribution installed on your system, see for instance *Open MPI* if necessary.

`library(pbdMPI)`

*Kursawe*
test function has two objectives of three variables.

```
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))
}
```

The variables lie in the range [-5, 5]:

```
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]
```

Both functions are to be minimized:

```
nobj <- 2 # number of objectives
minmax <- c(FALSE, FALSE) # minimization for both functions
```

Set algorithmic parameters for **caRamel**:

```
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
```