Performance comparison of evolutionary algorithms for airfoil design
Different evolutionary algorithms, by their very nature, will have different search trajectory characteristics. Understanding these particularly for real world problems gives researchers and practitioners valuable insights into potential problem domains for the various algorithms, as well as an unde...
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ftgriffithuniv:oai:research-repository.griffith.edu.au:10072/104584 2024-06-23T07:54:01+00:00 Performance comparison of evolutionary algorithms for airfoil design Randall, M Rawlins, T Lewis, A Kipouros, T 2015-06-01 to 2015-06-03 Reykjavik, Iceland 2015 http://hdl.handle.net/10072/104584 https://doi.org/10.1016/j.procs.2015.05.384 English eng Elsevier Procedia Computer Science ICCS 2015 http://hdl.handle.net/10072/104584 1877-0509 doi:10.1016/j.procs.2015.05.384 http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2015 The Authors. Published by Elsevier B.V. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited. You may not alter, transform, or build upon this work. open access Optimisation Information and computing sciences Aerodynamics (excl. hypersonic aerodynamics) Conference output 2015 ftgriffithuniv https://doi.org/10.1016/j.procs.2015.05.384 2024-05-29T00:10:31Z Different evolutionary algorithms, by their very nature, will have different search trajectory characteristics. Understanding these particularly for real world problems gives researchers and practitioners valuable insights into potential problem domains for the various algorithms, as well as an understanding for potential hybridisation. In this study, we examine three evolutionary techniques, namely, multi-objective particle swarm optimisation, extremal optimisation and tabu search. A problem that is to design optimal cross sectional areas of airfoils that maximise lift and minimise drag, is used. The comparison analyses actual parameter values, rather than just objective function values and computational costs. It reveals that the three algorithms had distinctive search patterns, and favoured different regions during exploration of the design space. Griffith Health, School of Human Services and Social Work Full Text Conference Object Iceland Griffith University: Griffith Research Online Griffith ENVELOPE(-155.500,-155.500,-85.883,-85.883) Procedia Computer Science 51 2267 2276 |
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Griffith University: Griffith Research Online |
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ftgriffithuniv |
language |
English |
topic |
Optimisation Information and computing sciences Aerodynamics (excl. hypersonic aerodynamics) |
spellingShingle |
Optimisation Information and computing sciences Aerodynamics (excl. hypersonic aerodynamics) Randall, M Rawlins, T Lewis, A Kipouros, T Performance comparison of evolutionary algorithms for airfoil design |
topic_facet |
Optimisation Information and computing sciences Aerodynamics (excl. hypersonic aerodynamics) |
description |
Different evolutionary algorithms, by their very nature, will have different search trajectory characteristics. Understanding these particularly for real world problems gives researchers and practitioners valuable insights into potential problem domains for the various algorithms, as well as an understanding for potential hybridisation. In this study, we examine three evolutionary techniques, namely, multi-objective particle swarm optimisation, extremal optimisation and tabu search. A problem that is to design optimal cross sectional areas of airfoils that maximise lift and minimise drag, is used. The comparison analyses actual parameter values, rather than just objective function values and computational costs. It reveals that the three algorithms had distinctive search patterns, and favoured different regions during exploration of the design space. Griffith Health, School of Human Services and Social Work Full Text |
format |
Conference Object |
author |
Randall, M Rawlins, T Lewis, A Kipouros, T |
author_facet |
Randall, M Rawlins, T Lewis, A Kipouros, T |
author_sort |
Randall, M |
title |
Performance comparison of evolutionary algorithms for airfoil design |
title_short |
Performance comparison of evolutionary algorithms for airfoil design |
title_full |
Performance comparison of evolutionary algorithms for airfoil design |
title_fullStr |
Performance comparison of evolutionary algorithms for airfoil design |
title_full_unstemmed |
Performance comparison of evolutionary algorithms for airfoil design |
title_sort |
performance comparison of evolutionary algorithms for airfoil design |
publisher |
Elsevier |
publishDate |
2015 |
url |
http://hdl.handle.net/10072/104584 https://doi.org/10.1016/j.procs.2015.05.384 |
op_coverage |
2015-06-01 to 2015-06-03 Reykjavik, Iceland |
long_lat |
ENVELOPE(-155.500,-155.500,-85.883,-85.883) |
geographic |
Griffith |
geographic_facet |
Griffith |
genre |
Iceland |
genre_facet |
Iceland |
op_relation |
Procedia Computer Science ICCS 2015 http://hdl.handle.net/10072/104584 1877-0509 doi:10.1016/j.procs.2015.05.384 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2015 The Authors. Published by Elsevier B.V. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited. You may not alter, transform, or build upon this work. open access |
op_doi |
https://doi.org/10.1016/j.procs.2015.05.384 |
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Procedia Computer Science |
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51 |
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2267 |
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2276 |
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1802645943209689088 |