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

July 18, 2018 ยท Declared Dead ยท ๐Ÿ› Journal of Statistical Software

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Authors Felipe Campelo, Lucas S. Batista, Claus Aranha arXiv ID 1807.06731 Category cs.NE: Neural & Evolutionary Citations 23 Venue Journal of Statistical Software Last Checked 4 months ago
Abstract
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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