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...

Full description

Saved in:
Bibliographic Details
Published in:Journal of statistical software 2020-02, Vol.92 (6), p.1-39
Main Authors: Campelo, Felipe, Batista, Lucas S., Aranha, Claus
Format: Article
Language:English
Subjects:
component-oriented design
moea/d
multiobjective evolutionary algorithms
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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