Performance comparison of evolutionary algorithms for airfoil design
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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.
International Conference On Computational Science, ICCS 2015 — Computational Science at the Gates of Nature
Copyright 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.
Aerodynamics (excl. Hypersonic Aerodynamics)
Neural, Evolutionary and Fuzzy Computation