Optimisation of airfoils using parallel genetic algorithms
This paper describes a parallel genetic algorithm which is linked to CFD analysis for the design of optimal airfoils. The method has been implemented on a variety of parallel architectures, and results to illustrate its application are presented. A common problem with genetic algorithms (or GAs) is how to maintain diversity of the gene pool and avoid premature convergence of the population. Subdivision of the population into semi-isolated subpopulations (commonly referred to as ‘demes’) not only helps significantly in this regard, but is ideally suited to implementation on a network of workstations.
KeywordsGenetic Algorithm Flow Solver Parallel Genetic Algorithm Airfoil Design Airfoil Optimisation
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- D. E. Goldberg: Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-Wesley (1988)Google Scholar
- C. Poloni: Ch.20 of Genetic Algorithms in Eng. and Comp. Sci., ed. G. Winter et al, Wiley (1995)Google Scholar
- K. Yamamoto and O. Inoue: AIAA-95-1650-CP (1995)Google Scholar
- R. Tanese: PhD thesis, U. Michigan (1989)Google Scholar
- D. J. Doorly: Ch. 13 of Genetic Algorithms in Eng. and Comp. Sci., ed. G. Winter et al, Wiley (1995)Google Scholar
- J. Nang and K. Matsuo: J. SICE, 33, 6, pp 500–509 (1994)Google Scholar
- J. Peraire, K. Morgan, and J. Peiró, AGARD Report 787, (1992)Google Scholar
- M. Drela, in ‘Low Reynolds Number Aerodynamics’ ed. T J Mueller, Lecture notes in Engineering 54, Springer Verlag (1989)Google Scholar