rmoo - R Multi-Objective Optimization

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Overview

A Non-Dominated Sorting based Multi-Objective Optimization package, built upon the ‘GA’ package.

‘rmoo’ provides a complete and flexible framework for optimizing multiple supplied objectives. You will have at your disposal a wide range of configuration options for the NSGA, NSGA-II and NSGA-III algorithms, as well as representation of real numbers, permutations and binaries.

Installation

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")

Usage

A simple example of running nsga3 solving the DTLZ1 problem:

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)
}

result <- nsga3(fitness = DTLZ1,
                type = "real-valued",
                lower = c(0,0,0),
                upper = c(1,1,1),
                popSize = 92,
                n_partitions = 12,
                maxiter = 300)
                
pcp(object = result)

#Scatter without optimal points
scatter(object = result)

#Scatter with optimal points (Using reference points as optimal points)
scatter(object = result, optimal = result@reference_points)

#Polar Coordinates
polar(fitness = result@fitness[1:3,])

#Headmap Plot
heat_map(fitness = result@fitness[1:3,])