Is NSGA-II Ready for Large-Scale Multi-Objective Optimization?
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...
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MDPI AG
2022-11-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/27/6/103 |
| _version_ | 1797456487668252672 |
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| author | Antonio J. Nebro Jesús Galeano-Brajones Francisco Luna Carlos A. Coello Coello |
| author_facet | Antonio J. Nebro Jesús Galeano-Brajones Francisco Luna Carlos A. Coello Coello |
| author_sort | Antonio J. Nebro |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>2</mn><mn>17</mn></msup><mo>=</mo><mn>131</mn><mo>,</mo><mn>072</mn></mrow></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-09T16:08:32Z |
| format | Article |
| id | doaj.art-e2ddce6e6a404b2987ddbffcb3656948 |
| institution | Directory Open Access Journal |
| issn | 1300-686X 2297-8747 |
| language | English |
| last_indexed | 2024-03-09T16:08:32Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
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| series | Mathematical and Computational Applications |
| spelling | doaj.art-e2ddce6e6a404b2987ddbffcb36569482023-11-24T16:30:55ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472022-11-0127610310.3390/mca27060103Is NSGA-II Ready for Large-Scale Multi-Objective Optimization?Antonio J. Nebro0Jesús Galeano-Brajones1Francisco Luna2Carlos A. Coello Coello3ITIS Software, University of Málaga, Ada Byron Research Building, 29071 Málaga, SpainDepartamento de Ingeniería de Sistemas Informáticos y Telemáticos, Universidad de Extremadura, Centro Universitario de Mérida, 06800 Badajoz, SpainITIS Software, University of Málaga, Ada Byron Research Building, 29071 Málaga, SpainEvolutionary Computation Group, CINVESTAV-IPN, Ciudad de México 07360, MexicoNSGA-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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mn>2</mn><mn>17</mn></msup><mo>=</mo><mn>131</mn><mo>,</mo><mn>072</mn></mrow></semantics></math></inline-formula> 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.https://www.mdpi.com/2297-8747/27/6/103NSGA-IIauto-configuration and auto-design of metaheuristicslarge-scale multi-objective optimizationreal-world problems optimization |
| spellingShingle | Antonio J. Nebro Jesús Galeano-Brajones Francisco Luna Carlos A. Coello Coello Is NSGA-II Ready for Large-Scale Multi-Objective Optimization? Mathematical and Computational Applications NSGA-II auto-configuration and auto-design of metaheuristics large-scale multi-objective optimization real-world problems optimization |
| title | Is NSGA-II Ready for Large-Scale Multi-Objective Optimization? |
| title_full | Is NSGA-II Ready for Large-Scale Multi-Objective Optimization? |
| title_fullStr | Is NSGA-II Ready for Large-Scale Multi-Objective Optimization? |
| title_full_unstemmed | Is NSGA-II Ready for Large-Scale Multi-Objective Optimization? |
| title_short | Is NSGA-II Ready for Large-Scale Multi-Objective Optimization? |
| title_sort | is nsga ii ready for large scale multi objective optimization |
| topic | NSGA-II auto-configuration and auto-design of metaheuristics large-scale multi-objective optimization real-world problems optimization |
| url | https://www.mdpi.com/2297-8747/27/6/103 |
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