no code implementations • 19 May 2023 • Mayowa Ayodele, Richard Allmendinger, Manuel López-Ibáñez, Arnaud Liefooghe, Matthieu Parizy
In this work, we extend the adaptive method based on averages in two ways: (i)~we extend the adaptive method of deriving scalarisation weights for problems with two or more objectives, and (ii)~we use an alternative measure of distance to improve performance.
no code implementations • 6 Jun 2021 • Richard Allmendinger, Andrzej Jaszkiewicz, Arnaud Liefooghe, Christiane Tammer
The presence of many objectives typically introduces a number of challenges that affect the choice/design of optimization algorithms.
no code implementations • 2 May 2021 • Johann Dreo, Arnaud Liefooghe, Sébastien Verel, Marc Schoenauer, Juan J. Merelo, Alexandre Quemy, Benjamin Bouvier, Jan Gmys
The success of metaheuristic optimization methods has led to the development of a large variety of algorithm paradigms.
no code implementations • 15 Apr 2020 • Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe, Ke Li, Qingfu Zhang
This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms.
no code implementations • 8 Feb 2020 • Xiaoran Ruan, Ke Li, Bilel Derbel, Arnaud Liefooghe
The effectiveness of our proposed algorithm is validated on benchmark problems with 10, 20, 50 variables, comparing with three state-of-the-art SAEAs.
no code implementations • 26 Sep 2014 • Hernan Aguirre, Arnaud Liefooghe, Sébastien Verel, Kiyoshi Tanaka
This work studies the behavior of three elitist multi- and many-objective evolutionary algorithms generating a high-resolution approximation of the Pareto optimal set.
no code implementations • 19 Sep 2014 • Bilel Derbel, Dimo Brockhoff, Arnaud Liefooghe, Sébastien Verel
Recently, there has been a renewed interest in decomposition-based approaches for evolutionary multiobjective optimization.
no code implementations • 19 Sep 2014 • Manuel López-Ibáñez, Arnaud Liefooghe, Sébastien Verel
Such local search algorithms typically return a set of mutually nondominated Pareto local optimal (PLO) solutions, that is, a PLO-set.