The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Jul 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1.
4 papers.
- 2022Search Trajectories Networks of Multiobjective Evolutionary Algorithmscited by 3
- 2021Faster Convergence in Multi-Objective Optimization Algorithms Based on Decompositioncited by 2
- 2022Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problemscited by 1
- 2023Multiobjective Evolutionary Component Effect on Algorithm behaviorcited by 0
- 2023Multiobjective Evolutionary Component Effect on Algorithm behavior31 Jul 2023
- 2022Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems25 Mar 2022
- 2022Search Trajectories Networks of Multiobjective Evolutionary Algorithms27 Jan 2022
- 2021Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition21 Dec 2021