2026-05-28T00:12:40Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/365892026-02-03T11:16:33Zcom_10630_2254col_10630_37953

Is NSGA-II Ready for Large-ScaleMulti-Objective Optimization? Nebro-Urbaneja, Antonio Jesús Galeano-Brajones, Jesús Luna-Valero, Francisco Coello Coello, Carlos A. Programación heurística Algoritmos genéticos Computación evolutiva Optimización matemática NSGA-II is, by far, the most popular metaheuristic that has been adopted for solving multi-objective optimization problems. However, its most common usage, particularly when dealing with continuous problems, is circumscribed to a standard algorithmic configuration similar to the one 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 highly configurable 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 realworld problem of the telecommunications domain. Our experiments reveal that an auto-configured version of NSGA-II can properly address test problems ZDT1 and ZDT2 with up to 217 = 131, 072 decision variables. The same methodology, when applied to the telecommunications problem, shows that significant improvements can be obtained with respect to the original NSGA-II algorithm when solving problems with thousands of bits. 2025-01-20T18:11:11Z 2025-01-20T18:11:11Z 2022-11-30 journal article 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 https://hdl.handle.net/10630/36589 10.3390/mca27060103 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI