Performance comparison of evolutionary algorithms for airfoil design

Marcus Randall, Tim Rawlins, Andrew Lewis, Timoleon Kipouros

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

7 Citations (Scopus)
25 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationInternational conference on computational science, ICCS 2015 Computational science at the gates of nature
EditorsS Koziel, L Leifsson, M Lees, VV Krzhizhanovskaya, J Dongarra, PMA Sloot
PublisherElsevier
Pages2267-2276
Number of pages10
Volume51
Edition1
DOIs
Publication statusPublished - 1 Jan 2015
Event15th Annual International Conference on Computational Science (ICCS) - Reykjavik, Reykjavik, Iceland
Duration: 1 Jun 20153 Jun 2015

Publication series

NameProcedia Computer Science
PublisherElsevier BV
ISSN (Print)1877-0509

Conference

Conference15th Annual International Conference on Computational Science (ICCS)
CountryIceland
CityReykjavik
Period1/06/153/06/15

Cite this

Randall, M., Rawlins, T., Lewis, A., & Kipouros, T. (2015). Performance comparison of evolutionary algorithms for airfoil design. In S. Koziel, L. Leifsson, M. Lees, VV. Krzhizhanovskaya, J. Dongarra, & PMA. Sloot (Eds.), International conference on computational science, ICCS 2015 Computational science at the gates of nature (1 ed., Vol. 51, pp. 2267-2276). (Procedia Computer Science). Elsevier. https://doi.org/10.1016/j.procs.2015.05.384
Randall, Marcus ; Rawlins, Tim ; Lewis, Andrew ; Kipouros, Timoleon. / Performance comparison of evolutionary algorithms for airfoil design. International conference on computational science, ICCS 2015 Computational science at the gates of nature. editor / S Koziel ; L Leifsson ; M Lees ; VV Krzhizhanovskaya ; J Dongarra ; PMA Sloot. Vol. 51 1. ed. Elsevier, 2015. pp. 2267-2276 (Procedia Computer Science).
@inproceedings{3a5382b757524b1091cd3fbd19156269,
title = "Performance comparison of evolutionary algorithms for airfoil design",
abstract = "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.",
author = "Marcus Randall and Tim Rawlins and Andrew Lewis and Timoleon Kipouros",
year = "2015",
month = "1",
day = "1",
doi = "10.1016/j.procs.2015.05.384",
language = "English",
volume = "51",
series = "Procedia Computer Science",
publisher = "Elsevier",
pages = "2267--2276",
editor = "S Koziel and L Leifsson and M Lees and VV Krzhizhanovskaya and J Dongarra and PMA Sloot",
booktitle = "International conference on computational science, ICCS 2015 Computational science at the gates of nature",
address = "Netherlands",
edition = "1",

}

Randall, M, Rawlins, T, Lewis, A & Kipouros, T 2015, Performance comparison of evolutionary algorithms for airfoil design. in S Koziel, L Leifsson, M Lees, VV Krzhizhanovskaya, J Dongarra & PMA Sloot (eds), International conference on computational science, ICCS 2015 Computational science at the gates of nature. 1 edn, vol. 51, Procedia Computer Science, Elsevier, pp. 2267-2276, 15th Annual International Conference on Computational Science (ICCS), Reykjavik, Iceland, 1/06/15. https://doi.org/10.1016/j.procs.2015.05.384

Performance comparison of evolutionary algorithms for airfoil design. / Randall, Marcus; Rawlins, Tim; Lewis, Andrew; Kipouros, Timoleon.

International conference on computational science, ICCS 2015 Computational science at the gates of nature. ed. / S Koziel; L Leifsson; M Lees; VV Krzhizhanovskaya; J Dongarra; PMA Sloot. Vol. 51 1. ed. Elsevier, 2015. p. 2267-2276 (Procedia Computer Science).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

TY - GEN

T1 - Performance comparison of evolutionary algorithms for airfoil design

AU - Randall, Marcus

AU - Rawlins, Tim

AU - Lewis, Andrew

AU - Kipouros, Timoleon

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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.

AB - 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.

UR - http://www.sciencedirect.com/journal/procedia-computer-science/vol/51/suppl/C

UR - http://www.scopus.com/inward/record.url?scp=84939172116&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2015.05.384

DO - 10.1016/j.procs.2015.05.384

M3 - Conference contribution

VL - 51

T3 - Procedia Computer Science

SP - 2267

EP - 2276

BT - International conference on computational science, ICCS 2015 Computational science at the gates of nature

A2 - Koziel, S

A2 - Leifsson, L

A2 - Lees, M

A2 - Krzhizhanovskaya, VV

A2 - Dongarra, J

A2 - Sloot, PMA

PB - Elsevier

ER -

Randall M, Rawlins T, Lewis A, Kipouros T. Performance comparison of evolutionary algorithms for airfoil design. In Koziel S, Leifsson L, Lees M, Krzhizhanovskaya VV, Dongarra J, Sloot PMA, editors, International conference on computational science, ICCS 2015 Computational science at the gates of nature. 1 ed. Vol. 51. Elsevier. 2015. p. 2267-2276. (Procedia Computer Science). https://doi.org/10.1016/j.procs.2015.05.384