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Multiobjective Optimization Using Adjoint Gradient Enhanced Approximation Models for Genetic Algorithms

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Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3984))

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Abstract

In this work, a multiobjective design optimization framework is developed by combining GAs and an approximation technique called Kriging method which can produce fairly accurate global approximations to the actual design space to provide the function evaluations efficiently. It is applied to a wing planform design problem and its results demonstrate the efficiency and applicability of the proposed design framework. Furthermore, to improve the efficiency of the propsed method using adjoint gradients two different approaches are tested. The results show that the adjoint gradient can efficiently replace computationally expensive sample data needed for constructing the Kriging models, and that the adjoint gradient-based optimization techniques can be utilized to refine the design candidates obtained through the approximation model based genetic algorithms.

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Author information

Authors and Affiliations

  1. Agency for Defense Development, Yuseong Daejeon, Republic of Korea

    Sangho Kim

  2. Republic of Korea Air Force Academy, Chungjoo, Republic of Korea

    Hyoung-Seog Chung

Authors
  1. Sangho Kim
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  2. Hyoung-Seog Chung
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary,, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. OptimaNumerics Ltd.,, Cathedral House 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  5. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  6. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  7. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  8. School of Information and Communication Engineering, Sungkyunkwan University, Korea

    Hyunseung Choo

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© 2006 Springer-Verlag Berlin Heidelberg

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Kim, S., Chung, HS. (2006). Multiobjective Optimization Using Adjoint Gradient Enhanced Approximation Models for Genetic Algorithms. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751649_54

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  • DOI: https://doi.org/10.1007/11751649_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34079-9

  • Online ISBN: 978-3-540-34080-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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