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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References
Muyl, F., Dumas, L., Herbert, V.: Hybrid method for aerodynamic shape optimization in automotive industry. Computers and Fluids 33, 849–858 (2004)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Poloni, C.: Hybrid GA for multi objective aerodynamic shape optimization. In: Genetic algorithms in engineering and computer science, vol. 33, pp. 397–415. John Wiley and Sons, Chichester (1995)
Renders, J.M., Flasse, S.P.: Hybrid methods using genetic algorithms for global optimization. IEEE Transactions on systems, man and cybernetics 26, 243–258 (1996)
Vicini, A., Quagliarella, D.: Airfoil and wing design through hybrid optimization strategies. AIAA paper (1998)
Ong, Y.S., Nair, P.B., Keane, A.J., Wong, K.W.: Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems. In: Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing Series, pp. 307–331. Springer, Heidelberg (2004)
Jin, Y.: A survey on fitness approximation in evolutionary computation. Journal of Soft Computing 9, 3–12 (2005)
Jin, Y., Olhofer, M., Sendhoff, B.: A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation 6, 481–494 (2002)
Ong, Y.S., Nair, P.B., Keane, A.J.: Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling. AIAA Journal 41, 687–696 (2003)
Giannakoglou, K.C.: Acceleration of GA using neural networks, theoretical background. GA for optimization in aeronautics and turbomachinery. VKI Lecture Series (2000)
Muyl, F.: Méthode d’optimisation hybrides: application à l’optimisation de formes arodynamiques automobiles. Phd thesis Université Paris 6 (2003)
Sagi, C.J., Han, T., Hammond, D.C.: Optimization of bluff body for minimum drag in ground proximity. AIAA paper (1992)
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Dumas, L., Herbert, V., Muyl, F. (2005). Comparison of Global Optimization Methods for Drag Reduction in the Automotive Industry. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_99
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DOI: https://doi.org/10.1007/11424925_99
Publisher Name: Springer, Berlin, Heidelberg
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