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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| Veröffentlicht in: | Journal of statistical software Jg. 92; H. 6; S. 1 - 39 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Foundation for Open Access Statistics
01.02.2020
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| Schlagworte: | |
| ISSN: | 1548-7660, 1548-7660 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | 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. |
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| ISSN: | 1548-7660 1548-7660 |
| DOI: | 10.18637/jss.v092.i06 |