Journal
EUROPEAN JOURNAL OF COMPUTATIONAL MECHANICS
Volume 17, Issue 1-2, Pages 103-126Publisher
TAYLOR & FRANCIS LTD
DOI: 10.3166/REMN.17.103-126
Keywords
aerodynamics; computational fluid dynamics; adjoint methods; genetic algorithms
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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.
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