RT Journal Article
T1 Is NSGA-II Ready for Large-ScaleMulti-Objective Optimization?
A1 Nebro-Urbaneja, Antonio Jesús
A1 Galeano-Brajones, Jesús
A1 Luna-Valero, Francisco
A1 Coello Coello, Carlos A.
K1 Programación heurística
K1 Algoritmos genéticos
K1 Computación evolutiva
K1 Optimización matemática
AB NSGA-II is, by far, the most popular metaheuristic that has been adopted for solvingmulti-objective optimization problems. However, its most common usage, particularly when dealingwith continuous problems, is circumscribed to a standard algorithmic configuration similar to theone described in its seminal paper. In this work, our aim is to show that the performance of NSGA-II,when properly configured, can be significantly improved in the context of large-scale optimization.It leverages a combination of tools for automated algorithmic tuning called irace, and a highlyconfigurable version of NSGA-II available in the jMetal framework. Two scenarios are devised: first,by solving the Zitzler–Deb–Thiele (ZDT) test problems, and second, when dealing with a binary realworldproblem of the telecommunications domain. Our experiments reveal that an auto-configuredversion of NSGA-II can properly address test problems ZDT1 and ZDT2 with up to 217 = 131, 072decision variables. The same methodology, when applied to the telecommunications problem, showsthat significant improvements can be obtained with respect to the original NSGA-II algorithm whensolving problems with thousands of bits.
PB MDPI
YR 2022
FD 2022-11-30
LK https://hdl.handle.net/10630/36589
UL https://hdl.handle.net/10630/36589
LA eng
NO Nebro, A.J.; Galeano- Brajones, J.; Luna, F.; Coello Coello, C.A. Is NSGA-II Ready for Large-Scale Multi-Objective Optimization? Math. Comput. Appl. 2022, 27, 103. https://doi.org/ 10.3390/mca27060103
NO Partial funding for open access charge: Universidad de Málaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026