A developed surrogate-based optimization framework combining HDMR-based modeling technique and TLBO algorithm for high-dimensional engineering problems

  • Xiaojing Wu
  • Xuhao Peng
  • Weisheng Chen
  • Weiwei ZhangEmail author
Research Paper


The traditional surrogate-based optimization techniques are facing severe challenges for high-dimensional engineering optimization problems. The main challenges are how to overcome metamodeling and optimization difficulties in high-dimensional design space. To solve the above difficulties, a developed surrogate-based optimization framework combining high-dimensional model representation (HDMR)-based modeling technique and teaching-learning-based optimization (TLBO) algorithm is developed. A high-dimensional model can be decomposed into a series of low-dimensional models by HDMR-based modeling technique, which can greatly reduce the difficulty of building high-dimensional model. The TLBO algorithm which has strong optimization ability and non-parameter setting characteristic is introduced to optimize the HDMR-based model to overcome the difficulty of optimization. Several representative functions are selected as examples to verify the developed optimization method for high-dimensional problems. In addition, The developed surrogate-based optimization is applied to solve a typical engineering optimization problem: high-dimensional aerodynamic shape optimization. It can be concluded that the optimization ability of the traditional surrogate-based optimization framework can be improved with assistance of HDMR-based modeling technique and TLBO algorithm for high-dimensional engineering problems.


High-dimensional engineering problems Surrogate model High-dimensional model representation (HDMR) Teaching-learning-based optimization (TLBO) Aerodynamic shape optimization 


Funding information

“111” project of China (No. B17037), the National Science Fund for Excellent Young Scholars (No. 11622220) supported this work, China Postdoctoral Science Foundation (2018 M643588).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xiaojing Wu
    • 1
    • 2
  • Xuhao Peng
    • 2
  • Weisheng Chen
    • 1
  • Weiwei Zhang
    • 2
    Email author
  1. 1.School of Aerospace Science and TechnologyXidian UniversityXi’anChina
  2. 2.School of AeronauticsNorthwestern Polytechnical UniversityXi’anChina

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