Multiobjective Optimization Using Adjoint Gradient Enhanced Approximation Models for Genetic Algorithms

  • Sangho Kim
  • Hyoung-Seog Chung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3984)


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.


Pareto Front Multiobjective Optimization AIAA Paper Latin Hypercube Sampling Kriging Model 
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 2006

Authors and Affiliations

  • Sangho Kim
    • 1
  • Hyoung-Seog Chung
    • 2
  1. 1.Agency for Defense DevelopmentYuseong DaejeonRepublic of Korea
  2. 2.Republic of Korea Air Force AcademyChungjooRepublic of Korea

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