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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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. |
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language | English |
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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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