Giagkiozis, I., Purshouse, R.C. and Fleming, P.J. (2013) An overview of population-based algorithms for multi-objective optimisation. International Journal of Systems Science, 46 (9). 1572 - 1599. ISSN 0020-7721
Abstract
In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013 Taylor & Francis. This is an author produced version of a paper subsequently published in International Journal of Systems Science. Uploaded in accordance with the publisher's self-archiving policy. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Systems Science on 20/8/2013, available online: http://wwww.tandfonline.com/10.1080/00207721.2013.823526 |
Keywords: | genetic algorithms; ant colony optimisation; particle swarm optimisation; differential evolution; artificial immune systems; estimation of distribution algorithms |
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: | 08 Oct 2015 14:05 |
Last Modified: | 25 Mar 2018 01:13 |
Published Version: | https://doi.org/10.1080/00207721.2013.823526 |
Status: | Published |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Identification Number: | 10.1080/00207721.2013.823526 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:86309 |