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

TL;DR

The MOEADr package provides a modular, component-based framework for implementing and testing various MOEA/D algorithms, facilitating reproducibility and rapid development in multiobjective optimization research.

Contribution

It introduces a standardized, modular software framework for MOEA/D algorithms, enhancing reproducibility and ease of development for researchers and practitioners.

Findings

Provides a flexible, component-oriented implementation of MOEA/D

Enables reproducibility of existing MOEA/D variants

Facilitates rapid development of new multiobjective algorithms

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.