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MaOEA

The goal of MaOEA is to facilitate easy hybridization of algorithms for many objective optimization. In the package, several algorithms are available: SMS-EMOA, NSGA-III, and MO-CMA-ES. Each of these algorithms can be accessed independently. Using the main function, the algorithms can be called for specific number of iterations. Alternatively, if the hybridization follows a more complex rule, users may prefer to call the algorithm directly in their optimization loop. This will call the algorithm (i.e., the offspring generation and selection scheme) for a single iteration.

The package uses PyGMO (https://esa.github.io/pagmo2/) to compute hypervolume and hypervolume contribution.

Installation

You can install the released version of MaOEA from CRAN with:

install.packages("MaOEA")

Please note that MaOEA requires the users to have installed Python (see https://www.python.org) and being able to use the PyGMO module. Installation instruction for PyGMO is available in https://esa.github.io/pagmo2/install.html. Users can also try to use the function provided in the package:

MaOEA::install_python_dependencies()

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Version

Install

install.packages('MaOEA')

Monthly Downloads

169

Version

0.6.2

License

GPL (>= 3)

Issues

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Maintainer

Dani Irawan

Last Published

August 31st, 2020

Functions in MaOEA (0.6.2)

DTLZ2

The DTLZ2 test function.
GetIGD

Get IGD value
AdaptiveNormalization

Objective space normalization.
DTLZ1

The DTLZ1 test function.
EvaluatePopulation

Evaluate objective value of a set of individuals
DTLZ4

The DTLZ4 test function.
DTLZ3

The DTLZ3 test function.
GetHypervolume

Compute hypervolume
SMSEMOA

S-Metric Selection EMOA
Normalize

Objective space normalization.
GetLeastContribution

Get least HV contribution
EvaluateIndividual

Evaluate objective values of a single individual
WFG1

The WFG1 test function.
createWeights

Das and Dennis's structured weight generation, normal boundary intersection (NBI).
MOCMAES

Multi-Objective CMA-ES
InitializePopulationLHS

Initialize population with Latin Hypercube Sampling
compute_R2mtch

Modified tchebyscheff R2-indicator
WFG8

The WFG8 test function.
WFG7

The WFG7 test function.
WFG4

The WFG4 test function.
WFG2

The WFG2 test function.
SMOCMAES

Steady-state Multi-Objective CMA-ES
MaOEA-package

Many-Objective Evolutionary Algorithm
compute_R2HVC

Modified tchebyscheff R2-indicator contribution designed to approximate HV
compute_R2HV

Modified powered tchebyscheff R2-indicator designed to approximate HV
GetLeastContributor

Get least HV contributor
WFG9

The WFG9 test function.
WFG5

The WFG5 test function.
NSGA3

Elitist Non-dominated Sorting Genetic Algorithm version III
WFG6

The WFG6 test function.
cmaes_gen

Generator for cmaes_gen class.
optimMaOEA

Elitist Non-dominated Sorting Genetic Algorithm version III
load_python_dependencies

Install python modules required by MaOEA: numpy and PyGMO
createWeightsSobol

Sobol sequence weights
install_python_dependencies

Install python modules required by MaOEA: numpy and PyGMO
GetHVContribution

Get HV contribution of all points.