Mo, Q., Fialho Vilas Boas Duro, J.A. and Purshouse, R.C. (2025) Surrogate strategies for scalarisation-based multi-objective Bayesian optimizers. In: Singh, H., Ray, T., Knowles, J., Li, X., Branke, J., Wang, B. and Oyama, A., (eds.) Evolutionary Multi-Criterion Optimization (EMO 2025). 13th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2025), 04-07 Mar 2025, Canberra, Australia. Lecture Notes in Computer Science, 15513 . Springer Cham ISBN 978-981-96-3538-2
Abstract
Scalarisation-based approaches to multi-objective Bayesian optimization, such as the seminal ParEGO algorithm, may be either single-surrogate or multi-surrogate. In the former case, a single surrogate model is built of the scalarised function; in the latter case, separate surrogates are built for each objective function. A recent study argued that the multi-surrogate approach should be preferred and presented empirical findings supportive of this case. However, these findings were based on an outdated approach to benchmarking algorithm performance and were limited to low-dimensional problems. In this study, we use the modern COCO benchmarking framework to analyse the performance of single-surrogate and multi-surrogate ParEGO algorithms and compare these to random sampling, Sobol space-filling, and the high performing optimizer known as TPB. Our findings broadly support the original findings for low-dimensional problems, but we find that multisurrogate ParEGO performs comparatively poorly in higher dimensions. TPB tends to outperform both ParEGOs, suggesting that initial budget investment in ideal and nadir point identification is a favourable strategy.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Author(s). Except as otherwise noted, this author-accepted version of a proceedings paper published in Evolutionary Multi-Criterion Optimization (EMO 2025) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Bayesian Optimization; Multi-objective Optimization; Benchmark Problems; Surrogate Modelling |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Funding Information: | Funder Grant number MEDICAL RESEARCH COUNCIL MR/S037578/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Jan 2025 10:12 |
Last Modified: | 03 Mar 2025 15:26 |
Status: | Published |
Publisher: | Springer Cham |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220554 |