2026-06-06T19:08:53Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/278532026-02-03T11:04:24Zcom_10630_2254col_10630_37953
On the automatic design of multi‑objective particle swarm optimizers: experimentation and analysis.
Nebro-Urbaneja, Antonio Jesús
López-Ibáñez, Manuel
García-Nieto, José Manuel
Coello Coello, Carlos A.
Algoritmos computacionales
Optimización matemática
Benchmarking
Calidad total
Research in multi-objective particle swarm optimizers (MOPSOs) progresses by proposing one new MOPSO at a time. In spite of the commonalities among different MOPSOs, it is often unclear which algorithmic components are crucial for explaining the performance of a particular MOPSO design. Moreover, it is expected that different designs may perform best on different problem families and identifying a best overall MOPSO is a challenging task. We tackle this challenge here by: (1) proposing AutoMOPSO, a flexible algorithmic template for designing MOPSOs with a design space that can instantiate thousands of
potential MOPSOs; and (2) searching for good-performing MOPSO designs given a family of training problems by means of an automatic configuration tool (irace). We apply this automatic design methodology to generate a MOPSO that significantly outperforms two state-of-the-art MOPSOs on four well-known bi-objective problem families. We also identify the key design choices and parameters of the winning MOPSO by means of ablation. FAutoMOPSO is publicly available as part of the jMetal framework.
2023-10-17T10:50:37Z
2023-10-17T10:50:37Z
2023
2023-10-09
journal article
Nebro, A.J., López-Ibáñez, M., García-Nieto, J. et al. On the automatic design of multi-objective particle swarm optimizers: experimentation and analysis. Swarm Intell (2023). https://doi.org/10.1007/s11721-023-00227-2
https://hdl.handle.net/10630/27853
10.1007/s11721-023-00227-2
eng
http://creativecommons.org/licenses/by-nc-nd/4.0/
open access
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Springer Nature