rmoo is a non-dominated sorting based multi-objective optimization
package built upon the ‘GA’
package. It provides a
comprehensive, flexible, and modular framework for multi/many-objective
optimization. Users have a wide range of configuration options at their
disposal, including the representation of real-values, permutations, and
binaries.
The algorithms available in rmoo include GA, NSGA-I, NSGA-II,
R-NSGA-II, and NSGA-III.
You can install the stable version on R CRAN:
install.packages("rmoo")Or you can install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("Evolutionary-Optimization-Laboratory/rmoo")All the algorithms implemented in rmoo are called throught rmoo
function. It will receive all the necessary parameters for execution. In
this simple example, we solve the DTLZ1 problem, using the NSGA-III:
library(rmoo)
DTLZ1 <- function (x, nobj = 3, ...)
{
if (is.null(dim(x))) {
x <- matrix(x, 1)
}
n <- ncol(x)
y <- matrix(x[, 1:(nobj - 1)], nrow(x))
z <- matrix(x[, nobj:n], nrow(x))
g <- 100 * (n - nobj + 1 + rowSums((z - 0.5)^2 - cos(20 *
pi * (z - 0.5))))
tmp <- t(apply(y, 1, cumprod))
tmp <- cbind(t(apply(tmp, 1, rev)), 1)
tmp2 <- cbind(1, t(apply(1 - y, 1, rev)))
f <- tmp * tmp2 * 0.5 * (1 + g)
return(f)
}
ref_points <- generate_reference_points(3,12)
result <- rmoo(fitness = DTLZ1,
type = "real-valued",
algorithm = "NSGA-III",
lower = c(0,0,0),
upper = c(1,1,1),
monitor = FALSE,
summary = FALSE,
parallel = FALSE,
nObj = 3,
reference_dirs = ref_points,
popSize = 92,
maxiter = 300)The rmoo package has a set of S4 method that can help the user to visualize, analysis, and interpret the results.
We are going to show how use the plot method, since depending on the
parameter it will display difference plots, e.g. passing “pcp” as
parameter for type, the method displays the Parallel Coordinate Plot:
plot(result, type="pcp")
Another example of plot method is when no parameters are passed, the method by default display the scatter plot. It evaluates the result dimensionality and depending on this, it will display 2-D, 3-D or Pairwise Scatter Plot.
#Scatter without optimal points
plot(result)
When displaying the scatter plot, reference points can be passed as parameters of the optimal argument, allowing the results to be compared to them:
#Scatter with optimal points (Using reference points as optimal points)
plot(result, optimal = result@reference_points)
Other plots available in rmoo are heatmap and polar coordinate, both
allow the user to view specific solutions.
#Polar Coordinates
plot(result, type="polar", individual = c(1:3))
#Heatmap Plot
plot(result, type="heatmap", individual = c(1:3))
Other methods available in rmoo that may be of interest are
getFitness(), getPopulation(), getMetrics(), print(),
summary() and others. We do not show the functionality of all these
functions since they will be detailed in depth in the future article and
vignettes.
Until the submission of the formal article that introduces rmoo, please cite using:
Benitez F., Pinto-Roa Diego P. (2023). rmoo: Multi-Objective Optimization in R. R package version 0.2.3 https://CRAN.R-project.org/package=rmoo/.
BibTeX entries for LaTeX users:
@Manual{,
title = {{rmoo}: Multi-Objective Optimization in R},
author = {Francisco Benitez and Diego P. {Pinto-Roa}},
year = {2023},
note = {R package version 0.2.3},
url = {https://CRAN.R-project.org/package=rmoo/},
}
or
@INPROCEEDINGS{10418638,
author={Francisco, J. Benítez and Diego, P. Pinto-Roa and Miguel, García-Torres and Parameshachari, B. D.},
booktitle={2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)},
title={A Hybrid Approach for Many-Objective Feature Selection in Intrusion Detection on Windows Operating Systems},
year={2023},
volume={},
number={},
pages={1-6},
doi={10.1109/CHILECON60335.2023.10418638/}}