A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization

A genetic algorithm is compared with a gradient-based (adjoint) algorithm in the context of several aerodynamic shape optimization problems. The examples include singlepoint and multipoint optimization problems, as well as the computation of a Pareto front. The results demonstrate that both algorith...

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Published in:European journal of computational mechanics Vol. 17; no. 1-2; pp. 103 - 126
Main Authors: Zingg, David W., Nemec, Marian, Pulliam, Thomas H.
Format: Journal Article
Language:English
French
Published: Taylor & Francis Group 01.01.2008
Subjects:
adjoint methods
aerodynamics
algorithmes génétiques
aérodynamique
computational fluid dynamics
genetic algorithms
mécanique des fluides numérique
méthodes de l'adjoint
ISSN:1779-7179, 1958-5829
Online Access:Get full text
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Abstract A genetic algorithm is compared with a gradient-based (adjoint) algorithm in the context of several aerodynamic shape optimization problems. The examples include singlepoint and multipoint optimization problems, as well as the computation of a Pareto front. The results demonstrate that both algorithms converge reliably to the same optimum. Depending on the nature of the problem, the number of design variables, and the degree of convergence, the genetic algorithm requires from 5 to 200 times as many function evaluations as the gradientbased algorithm.
AbstractList A genetic algorithm is compared with a gradient-based (adjoint) algorithm in the context of several aerodynamic shape optimization problems. The examples include singlepoint and multipoint optimization problems, as well as the computation of a Pareto front. The results demonstrate that both algorithms converge reliably to the same optimum. Depending on the nature of the problem, the number of design variables, and the degree of convergence, the genetic algorithm requires from 5 to 200 times as many function evaluations as the gradientbased algorithm.
Author Pulliam, Thomas H.
Zingg, David W.
Nemec, Marian
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  givenname: Thomas H.
  surname: Pulliam
  fullname: Pulliam, Thomas H.
  organization: NASA Ames Research Center
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Snippet A genetic algorithm is compared with a gradient-based (adjoint) algorithm in the context of several aerodynamic shape optimization problems. The examples...
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StartPage 103
SubjectTerms adjoint methods
aerodynamics
algorithmes génétiques
aérodynamique
computational fluid dynamics
genetic algorithms
mécanique des fluides numérique
méthodes de l'adjoint
Title A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization
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