Johnson, V., Duro, J.A. orcid.org/0000-0002-7684-4707, Kadirkamanathan, V. orcid.org/0000-0002-4243-2501 et al. (1 more author) (2023) Toward scalable benchmark problems for multi-objective multidisciplinary optimization. In: 2022 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 04-07 Dec 2022, Singapore, Singapore. Institute of Electrical and Electronics Engineers (IEEE) , pp. 133-140. ISBN 9781665487696
Abstract
Scalability in disciplines is an important consideration for multidisciplinary design optimization (MDO). Very few benchmark problems for multi-objective MDO exist in the literature, none of which are readily scalable. In this study, we introduce a new scalable benchmark problem that extends an existing well-known multi-objective benchmark problem. We show that scaling the number of disciplines in the problem, implying an increasing number of decision variables, does produce substantive changes in convergence ability. We also show that the accuracy of the multidisciplinary analysis (MDA) solver has an impact on the convergence ability of the multi-objective optimization algorithm, which is particularly noticeable when moving from 7 to 14 disciplines. Modification of standard (non-MDO) multi-objective benchmark problems is a promising approach to developing scalable multi-objective MDO benchmarks.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | multidisciplinary design optimization; multi-objective optimization; benchmark problems; scalability |
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) |
Funding Information: | Funder Grant number MEDICAL RESEARCH COUNCIL MR/S037578/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 28 Feb 2023 14:42 |
Last Modified: | 30 Jan 2024 01:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/ssci51031.2022.10022207 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:196802 |