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http://hdl.handle.net/1942/36612
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DC Field | Value | Language |
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dc.contributor.author | Loka, Nasrulloh | - |
dc.contributor.author | Couckuyt, Ivo | - |
dc.contributor.author | Garbuglia, Federico | - |
dc.contributor.author | Spina, Domenico | - |
dc.contributor.author | VAN NIEUWENHUYSE, Inneke | - |
dc.contributor.author | Dhaene , Tom | - |
dc.date.accessioned | 2022-02-07T10:15:44Z | - |
dc.date.available | 2022-02-07T10:15:44Z | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2022-02-03T19:48:37Z | - |
dc.identifier.citation | ENGINEERING WITH COMPUTERS, 9 , p. 1923 - 1933 | - |
dc.identifier.uri | http://hdl.handle.net/1942/36612 | - |
dc.description.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. | - |
dc.description.sponsorship | This work has been supported by the Flemish Government under the ”Onderzoeksprogramma Artifciële Intelligentie (AI) Vlaanderen” and the ”Fonds Wetenschappelijk Onderzoek (FWO)” programmes. | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.rights | The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 | - |
dc.subject.other | Multi-objective optimization | - |
dc.subject.other | Bayesian optimization | - |
dc.subject.other | Hypervolume | - |
dc.subject.other | Gaussian process | - |
dc.title | Bi-objective Bayesian optimization of engineering problems with cheap and expensive cost functions | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 1933 | - |
dc.identifier.spage | 1923 | - |
dc.identifier.volume | 39 | - |
local.format.pages | 11 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Loka, N (corresponding author), Ghent Univ Imec, Dept Informat Technol INTEC, IDLab, iGent, Techno Pk Zwijnaarde 126, B-9052 Ghent, Belgium. | - |
dc.description.notes | nasrulloh.loka@ugent.be | - |
local.publisher.place | ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1007/s00366-021-01573-7 | - |
dc.identifier.isi | WOS:000746293500001 | - |
local.provider.type | wosris | - |
local.description.affiliation | [Loka, Nasrulloh; Couckuyt, Ivo; Garbuglia, Federico; Spina, Domenico; Dhaene, Tom] Ghent Univ Imec, Dept Informat Technol INTEC, IDLab, iGent, Techno Pk Zwijnaarde 126, B-9052 Ghent, Belgium. | - |
local.description.affiliation | [Van Nieuwenhuyse, Inneke] Hasselt Univ, Res Grp Logist, Agoralaan Gebouw D, B-3590 Limburg, Belgium. | - |
local.uhasselt.international | no | - |
item.fulltext | With Fulltext | - |
item.contributor | Loka, Nasrulloh | - |
item.contributor | Couckuyt, Ivo | - |
item.contributor | Garbuglia, Federico | - |
item.contributor | Spina, Domenico | - |
item.contributor | VAN NIEUWENHUYSE, Inneke | - |
item.contributor | Dhaene , Tom | - |
item.accessRights | Open Access | - |
item.validation | ecoom 2023 | - |
item.fullcitation | Loka, Nasrulloh; Couckuyt, Ivo; Garbuglia, Federico; Spina, Domenico; VAN NIEUWENHUYSE, Inneke & Dhaene , Tom (2023) Bi-objective Bayesian optimization of engineering problems with cheap and expensive cost functions. In: ENGINEERING WITH COMPUTERS, 9 , p. 1923 - 1933. | - |
crisitem.journal.issn | 0177-0667 | - |
crisitem.journal.eissn | 1435-5663 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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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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