Is NSGA-II Ready for Large-Scale Multi-Objective Optimization?
| Autor: | Antonio J. Nebro, Jesús Galeano-Brajones, Francisco Luna, Carlos A. Coello Coello |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Mathematical and Computational Applications, Vol 27, Iss 6, p 103 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2297-8747 1300-686X |
| DOI: | 10.3390/mca27060103 |
| Popis: | 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 real-world 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. |
| Databáze: | Directory of Open Access Journals |
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