RT Journal Article
T1 On the automatic design of multi‑objective particle swarm optimizers: experimentation and analysis.
A1 Nebro-Urbaneja, Antonio Jesús
A1 López-Ibáñez, Manuel
A1 García-Nieto, José Manuel
A1 Coello Coello, Carlos A.
K1 Algoritmos computacionales
K1 Optimización matemática
K1 Benchmarking
K1 Calidad total
AB 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 ofpotential 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.
PB Springer Nature
YR 2023
FD 2023-10-09
LK https://hdl.handle.net/10630/27853
UL https://hdl.handle.net/10630/27853
LA eng
NO 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
NO Funding for  open  access  charge: Universidad  Málaga / CBUA.  This  work  has  been  partially  funded  by  the  Spanish  Ministry  of  Science  and  Innovation  via  Grant  PID2020-112540RB-C41  (AEI/FEDER, UE). Carlos A. Coello Coello gratefully acknowledges support from CONACyT Grant No. 2016-01-1920 (Investigación en Fronteras de la Ciencia 2016)
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026