Observations in applying Bayesian versus evolutionary approaches and their hybrids in parallel time-constrained optimization
Parallel Surrogate-Based Optimization (PSBO) is an efficient approach to deal with black-box time-consuming objective functions. According to the available computational budget to solve a given problem, three classes of algorithms are investigated and opposed in this paper: Bayesian Optimization Alg...
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| Published in: | Engineering applications of artificial intelligence Vol. 137; p. 109075 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.11.2024
Elsevier |
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| ISSN: | 0952-1976 |
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| Abstract | Parallel Surrogate-Based Optimization (PSBO) is an efficient approach to deal with black-box time-consuming objective functions. According to the available computational budget to solve a given problem, three classes of algorithms are investigated and opposed in this paper: Bayesian Optimization Algorithms (BOAs), Surrogate-Assisted Evolutionary Algorithms (SAEAs) and Surrogate-free Evolutionary Algorithms (EAs). A large set of benchmark functions and engineering applications are considered with various computational budgets. In this paper, we come up with guidelines for the choice between the three categories. According to the computational expensiveness of the objective functions and the number of processing cores, we identify a threshold from which SAEAs should be preferred to BOAs. Based on this threshold, we derive a new hybrid Bayesian/Evolutionary algorithm that allows one to tackle a wide range of problems without prior knowledge of their characteristics. |
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| AbstractList | Parallel Surrogate-Based Optimization (PSBO) is an efficient approach to deal with black-box time-consuming objective functions. According to the available computational budget to solve a given problem, three classes of algorithms are investigated and opposed in this paper: Bayesian Optimization Algorithms (BOAs), Surrogate-Assisted Evolutionary Algorithms (SAEAs) and Surrogate-free Evolutionary Algorithms (EAs). A large set of benchmark functions and engineering applications are considered with various computational budgets. In this paper, we come up with guidelines for the choice between the three categories. According to the computational expensiveness of the objective functions and the number of processing cores, we identify a threshold from which SAEAs should be preferred to BOAs. Based on this threshold, we derive a new hybrid Bayesian/Evolutionary algorithm that allows one to tackle a wide range of problems without prior knowledge of their characteristics. |
| ArticleNumber | 109075 |
| Author | Briffoteaux, Guillaume Tuyttens, Daniel Gobert, Maxime Melab, Nouredine Gmys, Jan |
| Author_xml | – sequence: 1 givenname: Maxime orcidid: 0000-0003-4925-4995 surname: Gobert fullname: Gobert, Maxime email: maxime.gobert@umons.ac.be organization: University of Mons, Faculty of Engineering, 9 rue de Houdain, Mons, 7000, Belgium – sequence: 2 givenname: Guillaume orcidid: 0000-0002-4878-2000 surname: Briffoteaux fullname: Briffoteaux, Guillaume email: guillaume.briffoteaux@umons.ac.be organization: University of Mons, Faculty of Engineering, 9 rue de Houdain, Mons, 7000, Belgium – sequence: 3 givenname: Jan orcidid: 0000-0001-9635-4396 surname: Gmys fullname: Gmys, Jan email: jan.gmys@univ-lille.fr organization: University of Lille - CNRS CRIStAL, Cité Scientifique, Villeneuve d’Ascq, 59650, France – sequence: 4 givenname: Nouredine orcidid: 0000-0003-1526-006X surname: Melab fullname: Melab, Nouredine email: nouredine.melab@univ-lille.fr organization: University of Lille - CNRS CRIStAL, Cité Scientifique, Villeneuve d’Ascq, 59650, France – sequence: 5 givenname: Daniel orcidid: 0000-0002-2567-1324 surname: Tuyttens fullname: Tuyttens, Daniel email: daniel.tuyttens@umons.ac.be organization: University of Mons, Faculty of Engineering, 9 rue de Houdain, Mons, 7000, Belgium |
| BackLink | https://hal.science/hal-04707543$$DView record in HAL |
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| Cites_doi | 10.3390/a15120446 10.1016/j.neucom.2015.02.088 10.1007/BF01589116 10.1016/j.paerosci.2005.02.001 10.3390/machines9120341 10.1016/j.swevo.2023.101293 10.1007/s11721-007-0002-0 10.1109/TEVC.2017.2675628 10.1109/JPROC.2015.2494218 10.1007/s00158-016-1432-3 10.1007/s13748-014-0051-8 10.21105/joss.02338 10.1007/s10898-016-0449-x 10.1023/A:1008306431147 10.1007/s10898-012-9892-5 10.1016/j.tcs.2011.02.016 10.1109/4235.985692 10.1016/j.swevo.2020.100693 10.1109/TAP.2004.825102 10.1198/016214508000000689 10.1007/978-3-030-70281-6_10 10.1016/j.swevo.2011.02.002 10.1016/j.swevo.2020.100717 10.1109/MCSE.2021.3083216 10.1007/s11390-020-9487-4 10.3934/mbe.2022512 10.1109/4235.996017 10.1155/2016/9420460 10.1016/j.future.2020.07.005 |
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| Keywords | Surrogate-based optimization Parallel computing 68W10 Evolutionary algorithms Bayesian optimization 65Y05 46N10 g74P99 |
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