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

  • Laurent Dumas
  • Vincent Herbert
  • Frédérique Muyl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3483)


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.


Genetic Algorithm Hybrid Method Drag Reduction Global Optimization Method Simple Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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