The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition
This provides a standardized tool for researchers and practitioners in optimization to improve reproducibility and accelerate algorithm development, though it is incremental as it builds on existing MOEA/D methods.
The authors tackled the challenge of reproducibility and development in Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) by introducing the MOEADr package, a component-based framework that standardizes and modularizes variants, enabling easier replication and faster testing of new algorithms.
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.