Jones, O., Oakley, J.E. and Purshouse, R. orcid.org/0000-0001-5880-1925 (2018) Component-level study of a decomposition-based multi-objective optimizer on a limited evaluation budget. In: Aquirre, H., (ed.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018). Genetic and Evolutionary Computation Conference (GECCO 2018), 15-19 Jul 2018, Kyoto, Japan. ACM , pp. 689-696. ISBN 978-1-4503-5618-3
Abstract
Decomposition-based algorithms have emerged as one of the most popular classes of solvers for multi-objective optimization. Despite their popularity, a lack of guidance exists for how to configure such algorithms for real-world problems, based on the features or contexts of those problems. One context that is important for many real-world problems is that function evaluations are expensive, and so algorithms need to be able to provide adequate convergence on a limited budget (e.g. 500 evaluations). This study contributes to emerging guidance on algorithm configuration by investigating how the convergence of the popular decomposition-based optimizer MOEA/D, over a limited budget, is affected by choice of component level configuration. Two main aspects are considered: (1) impact of sharing information; (2) impact of normalisation scheme. The empirical test framework includes detailed trajectory analysis, as well as more conventional performance indicator analysis, to help identify and explain the behaviour of the optimizer. Use of neighbours in generating new solutions is found to be highly disruptive for searching on a small budget, leading to better convergence in some areas but far worse convergence in others. The findings also emphasise the challenge and importance of using an appropriate normalisation scheme.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2018 The Authors. This is an author produced version of a paper subsequently published in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018). Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | MOEA/D; decomposition-based multi-objective optimization; component study; trajectory analysis |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 May 2018 11:56 |
Last Modified: | 22 Nov 2018 11:54 |
Published Version: | https://doi.org/10.1145/3205455.3205649 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3205455.3205649 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129995 |