Johnson, V., Duro, J., Kadirkamanathan, V. et al. (1 more author) (2023) A distributed multi-disciplinary design optimization benchmark test suite with constraints and multiple conflicting objectives. In: Paquete, L., (ed.) Genetic and Evolutionary Computation Conference Companion (GECCO '23 Companion), July 15--19, 2023, Lisbon, Portugal. Genetic and Evolutionary Computation Conference (GECCO 2023), 15-19 Jul 2023, Lisbon, Portugal. Association for Computing Machinery , New York, NY, United States , pp. 1611-1619. ISBN 979-8-4007-0120-7
Abstract
Collaborative optimization (CO) is an architecture within the multi-disciplinary design optimization (MDO) paradigm that partitions a constrained optimization problem into system and subsystem problems, with couplings between them. Multi-objective CO has multiple objectives at the system level and inequality constraints at the subsystem level. Whilst CO is an established technique, there are currently no scalable, constrained benchmark problems for multi-objective CO. In this study, we extend recent methods for generating scalable MDO benchmarks to propose a new benchmark test suite for multi-objective CO that is scalable in disciplines and variables, called `CO-ZDT'. We show that overly-constraining the number of generations in each iteration of the system-level optimizer leads to poor consistency constraint satisfaction. Increasing the number of subsystems in each of the problems leads to increasing system-level constraint violation. In problems with two subsystems, we find that convergence to the global Pareto front is very sensitive to the complexity of the landscape of the original non-decomposed problem. As the number of subsystems increases, convergence issues are encountered even for the simpler problem landscapes.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, https://doi.org/10.1145/3583133.3596414 |
Keywords: | constrained optimization; multi-disciplinary design; collaborative optimization |
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: | 26 Jul 2023 11:24 |
Last Modified: | 26 Jul 2023 11:31 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Refereed: | Yes |
Identification Number: | 10.1145/3583133.3596414 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201771 |