Abstract
This paper outlines the application of evolutionary search methods to problems in aeronautical design optimisation. The procedures described are based on the genetic algorithm (GA) and may be applied to other areas. Although easy to implement, a simple genetic algorithm is often found in applications to be of low effciency and to suffer from premature convergence. To improve performance, two alternative strategies are investigated. In the first, a learning classifier scheme is used to tune the GA for a particular class of problems. The second strategy uses a parallel distributed genetic algorithm supervised by single or competing agents. The implementation of each procedure, and results for typical design problems are outlined. The agent supervised distributed genetic algorithm is found to provide a model with a very high degree of adaptibility, and to lead to considerably improved efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Goldberg D E, Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-Wesley, 1988.
Bäck T, Evolutionary Algorithms in Theory and practice, Oxford, 1996.
Schwefel H P, Evolution and Optimum Seeking, Wilrey New York, 1995.
Davis L, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York 1991.
Obayashi, S., Yamaguchi Y., and Nakamura, T. Multiobjective genetic algorithm for multidisciplinary design of transonic wing planfoem, J. Aircraft 34, 5, pp 690–693, 1997
Doorly D J, Ch. 13 of Genetic Algorithms in Engineering and Computer Science, ed. G. Winter et al., Wiley, 1995.
Quagliarella D and DellaCioppa A, Genetic Algorithms Applied to the Aerodynamic Design of Transonic Airfoils, J. Aircraft 32, 889–891, 1995.
Poloni C, Ch. 20 of Genetic Algorithms in Eng. and Comp. Sci., ed. G. Winter et al., Wiley, 1995.
Yamamoto K, and Inoue O, Applications of Genetic Algorithms to Aerodynamic Shape Optimisation, AIAA-95-1650-CP, 1995
Tanese R, Distributed Genetic Algorithms, PhD thesis, U. Michigan, 1989.
Doorly D J, Peiró J, Kuan T, and Oesterle J-P, Optimisation of Airfoils Using Parallel Genetic Algorithms, in Proc. 15th Int. Conf. Num. Meth. Fluid Dyn., Monterey, 1996.
Nang J and Matsuo K, A Survey of Parallel Genetic Algorithms, J. SICE 33, 6, 500–509, 1994.
Doorly D J, Peiró J, and Oesterle J-P, Optimisation of Aerodynamic and Coupled Aerodynamic-Structural Design using Parallel Genetic Algorithms, in Proc. Sixth AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 401–409, 1996.
Holland J H, Adaptation in Natural and Artificial Systems, MIT Press, 1992.
Doorly D J and Peió, Supervised parallel genetic algorithms in Aerodynamic Optimisation, AIAA paper 97-1852, 1997.
Oesterle J-P, Aeronautical optimisation using parallel genetic algorithms, MSc thesis, Aeronautics Dept., Imperial College London, 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Doorly, D.J., Spooner, S., Peiró, J. (2000). Supervised Evolutionary Methods in Aerodynamic Design Optimisation. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_35
Download citation
DOI: https://doi.org/10.1007/3-540-45561-2_35
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67353-8
Online ISBN: 978-3-540-45561-5
eBook Packages: Springer Book Archive