Duro, J., Yan, Y., Purshouse, R. orcid.org/0000-0001-5880-1925 et al. (1 more author) (2018) Collaborative Multi-Objective Optimization for Distributed Design of Complex Products. In: Proceedings of the Genetic and Evolutionary Computation Conference 2018. GECCO '18 Genetic and Evolutionary Computation Conference, 15-19 Jul 2018, Kyoto. ACM ISBN 978-1-4503-5618-3
Abstract
Multidisciplinary design optimization problems with competing objectives that involve several interacting components can be called complex systems. Nowadays, it is common to partition the optimization problem of a complex system into smaller subsystems, each with a subproblem, in part because it is too difficult to deal with the problem all-at-once. Such an approach is suitable for large organisations where each subsystem can have its own (specialised) design team. However, this requires a design process that facilitates collaboration, and decision making, in an environment where teams may exchange limited information about their own designs, and also where the design teams work at different rates, have different time schedules, and are normally not co-located. A multiobjective optimization methodology to address these features is described. Subsystems exchange information about their own optimal solutions on a peer-to-peer basis, and the methodology enables convergence to a set of optimal solutions that satisfy the overall system. This is demonstrated on an example problem where the methodology is shown to perform as well as the ideal, but “unrealistic” approach, that treats the optimization problem all-at-once.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery. This is an author-produced version of a paper accepted for publication. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Collaborative multidisciplinary optimization; Complex systems; Multiobjective evolutionary algorithms; Multiple-criteria decision-making |
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 Apr 2018 08:52 |
Last Modified: | 20 Aug 2018 12:21 |
Published Version: | https://doi.org/10.1145/3205455.3205579 |
Status: | Published online |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3205455.3205579 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129994 |