Comparison of Global Optimization Methods for Drag Reduction in the Automotive Industry

  • Laurent Dumas
  • Vincent Herbert
  • Frédérique Muyl
Conference paper

DOI: 10.1007/11424925_99

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3483)
Cite this paper as:
Dumas L., Herbert V., Muyl F. (2005) Comparison of Global Optimization Methods for Drag Reduction in the Automotive Industry. In: Gervasi O. et al. (eds) Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg

Abstract

Various global optimization methods are compared in order to find the best strategy to solve realistic drag reduction problems in the automotive industry. All the methods consist in improving classical genetic algorithms, either by coupling them with a deterministic descent method or by incorporating a fast but approximated evaluation process. The efficiency of these methods (called HM and AGA respectively) is shown and compared, first on analytical test functions, then on a drag reduction problem where the computational time of a GA is reduced by a factor up to 7.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Laurent Dumas
    • 1
  • Vincent Herbert
    • 1
  • Frédérique Muyl
    • 2
  1. 1.Laboratoire Jacques-Louis LionsUniversité Pierre et Marie CurieParis Cedex 05France
  2. 2.PSA Peugeot CitroënVélizy VillacoublayFrance

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