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http://hdl.handle.net/1942/36612
Title: | Bi-objective Bayesian optimization of engineering problems with cheap and expensive cost functions | Authors: | Loka, Nasrulloh Couckuyt, Ivo Garbuglia, Federico Spina, Domenico VAN NIEUWENHUYSE, Inneke Dhaene , Tom |
Issue Date: | 2023 | Publisher: | SPRINGER | Source: | ENGINEERING WITH COMPUTERS, 9 , p. 1923 - 1933 | Abstract: | Multi-objective optimization of complex engineering systems is a challenging problem. The design goals can exhibit dynamic and nonlinear behaviour with respect to the system's parameters. Additionally, modern engineering is driven by simulation-based design which can be computationally expensive due to the complexity of the system under study. Bayesian optimization (BO) is a popular technique to tackle this kind of problem. In multi-objective BO, a data-driven surrogate model is created for each design objective. However, not all of the objectives may be expensive to compute. We develop an approach that can deal with a mix of expensive and cheap-to-evaluate objective functions. As a result, the proposed technique offers lower complexity than standard multi-objective BO methods and performs significantly better when the cheap objective function is difficult to approximate. In particular, we extend the popular hypervolume-based Expected Improvement (EI) and Probability of Improvement (POI) in bi-objective settings. The proposed methods are validated on multiple benchmark functions and two real-world engineering design optimization problems, showing that it performs better than its non-cheap counterparts. Furthermore, it performs competitively or better compared to other optimization methods. | Notes: | Loka, N (corresponding author), Ghent Univ Imec, Dept Informat Technol INTEC, IDLab, iGent, Techno Pk Zwijnaarde 126, B-9052 Ghent, Belgium. nasrulloh.loka@ugent.be |
Keywords: | Multi-objective optimization;Bayesian optimization;Hypervolume;Gaussian process | Document URI: | http://hdl.handle.net/1942/36612 | ISSN: | 0177-0667 | e-ISSN: | 1435-5663 | DOI: | 10.1007/s00366-021-01573-7 | ISI #: | WOS:000746293500001 | Rights: | The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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Bi-objective Bayesian optimization of engineering problems with cheap and expensive cost functions.pdf Restricted Access | Published version | 1.8 MB | Adobe PDF | View/Open Request a copy |
Cheap_Expensive_BO.pdf | Peer-reviewed author version | 926 kB | Adobe PDF | View/Open |
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