Wu, K.E. and Panoutsos, G. orcid.org/0000-0002-7395-8418 (2021) A visualisation method for Pareto Front approximations in many-objective optimisation. In: Proceedings of 2021 IEEE Congress on Evolutionary Computation (CEC). 2021 IEEE Congress on Evolutionary Computation (CEC), 28 Jun - 01 Jul 2021, Kraków, Poland (virtual conference). IEEE , pp. 1929-1937. ISBN 9781728183947
Abstract
Visualisation of Pareto Front (PF) approximations of many-objective optimisation problems (MaOP) is critical in understanding and solving a MaOP. Research is ongoing on developing effective visualisation methods with desired properties, such as simultaneously revealing dominance relations, PF shape, and the diversity of approximations. State-of-the-art visualisation methods in the literature often retain some of the preferred properties, but there are still shortfalls to address others. A new visualisation method is proposed in this paper, which covers the majority of the desired properties for visualisation methods. The proposed method is based on displaying PF approximations via projections on a reference vector versus distances to the same reference vector. The reference vector is created using nominal Ideal and Nadir points of existing nondominated PF approximation sets. MaF benchmark problems are used to demonstrate the effectiveness; results show that the proposed method exhibits a more balanced performance than the state-of-the-art in capturing desired visualisation properties.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 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: | Many-objective Optimisation; Performance indicator; Diversity; Reference vectors; Benchmark testing |
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: | 07 May 2021 08:36 |
Last Modified: | 09 Aug 2022 00:14 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/cec45853.2021.9504904 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173654 |