Salomon, S., Domínguez-Medina, C., Avigad, G. et al. (4 more authors) (2014) PSA based multi objective evolutionary algorithms. In: EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III. EVOLVE, August 2012, Mexico City, Mexico. Studies in Computational Intelligence, 500 . Springer , pp. 233-259.
Abstract
It has generally been acknowledged that both proximity to the Pareto front and a certain diversity along the front, should be targeted when using evolutionary multiobjective optimization. Recently, a new partitioning mechanism, the Part and Select Algorithm (PSA), has been introduced. It was shown that this partitioning allows for the selection of a well-diversified set out of an arbitrary given set, while maintaining low computational cost. When embedded into an evolutionary search (NSGA-II), the PSA has significantly enhanced the exploitation of diversity. In this paper, the ability of the PSA to enhance evolutionary multiobjective algorithms (EMOAs) is further investigated. Two research directions are explored here. The first one deals with the integration of the PSA within an EMOA with a novel strategy. Contrary to most EMOAs, that give a higher priority to proximity over diversity, this new strategy promotes the balance between the two. The suggested algorithm allows some dominated solutions to survive, if they contribute to diversity. It is shown that such an approach substantially reduces the risk of the algorithm to fail in finding the Pareto front. The second research direction explores the use of the PSA as an archiving selection mechanism, to improve the averaged Hausdorff distance obtained by existing EMOAs. It is shown that the integration of the PSA into NSGA-II-I and Δ p -EMOA as an archiving mechanism leads to algorithms that are superior to base EMOAS on problems with disconnected Pareto fronts. © 2014 Springer International Publishing Switzerland.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 Springer International Publishing Switzerland. This is an author produced version of a paper subsequently published in Studies in Computational Intelligence. Uploaded in accordance with the publisher's self-archiving policy. |
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 2016 15:25 |
Last Modified: | 30 Mar 2018 21:02 |
Published Version: | http://dx.doi.org/10.1007/978-3-319-01460-9_11 |
Status: | Published |
Publisher: | Springer |
Series Name: | Studies in Computational Intelligence |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-01460-9_11 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:98424 |