The MOEADr Package: A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition

Multiobjective evolutionary algorithms based on decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-orien...

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Bibliographic Details
Published in:	Journal of statistical software Vol. 92; no. 6; pp. 1 - 39
Main Authors:	Campelo, Felipe, Batista, Lucas S., Aranha, Claus
Format:	Journal Article
Language:	English
Published:	Foundation for Open Access Statistics 01.02.2020
Subjects:	component-oriented design moea/d multiobjective evolutionary algorithms
ISSN:	1548-7660, 1548-7660
Online Access:	Get full text
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Summary:	Multiobjective evolutionary algorithms based on decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework. This approach contributes for easier reproducibility of existing MOEA/D variants from the literature, as well as for faster development and testing of new composite algorithms. The package offers an standardized, modular implementation of MOEA/D based on this framework, which was designed aiming at providing researchers and practitioners with a standard way to discuss and express MOEA/D variants. In this paper we introduce the design principles behind the MOEADr package, as well as its current components. Three case studies are provided to illustrate the main aspects of the package.
ISSN:	1548-7660 1548-7660
DOI:	10.18637/jss.v092.i06