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Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems

Author

Listed:
  • Zan Yang

    (Huazhong University of Science and Technology)

  • Haobo Qiu

    (Huazhong University of Science and Technology)

  • Liang Gao

    (Huazhong University of Science and Technology)

  • Chen Jiang

    (Huazhong University of Science and Technology)

  • Jinhao Zhang

    (Huazhong University of Science and Technology)

Abstract

Surrogate-assisted evolutionary algorithms (SAEAs) have recently shown excellent ability in solving computationally expensive optimization problems. However, with the increase of dimensions of research problems, the effectiveness of SAEAs for high-dimensional problems still needs to be improved further. In this paper, a two-layer adaptive surrogate-assisted evolutionary algorithm is proposed, in which three different search strategies are adaptively executed during the iteration according to the feedback information which is proposed to measure the status of the algorithm approaching the optimal value. In the proposed method, the global GP model is used to pre-screen the offspring produced by the DE/current-to-best/1 strategy for fast convergence speed, and the DE/current-to-randbest/1 strategy is proposed to guide the global GP model to locate promising regions when the feedback information reaches a presetting threshold. Moreover, a local search strategy (DE/best/1) is used to guide the local GP model which is built by using individuals closest to the current best individual to intensively exploit the promising regions. Furthermore, a dimension reduction technique is used to construct a reasonably accurate GP model for high-dimensional expensive problems. Empirical studies on benchmark problems with 50 and 100 variables demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems under a limited computational budget.

Suggested Citation

  • Zan Yang & Haobo Qiu & Liang Gao & Chen Jiang & Jinhao Zhang, 2019. "Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems," Journal of Global Optimization, Springer, vol. 74(2), pages 327-359, June.
    • Handle: RePEc:spr:jglopt:v:74:y:2019:i:2:d:10.1007_s10898-019-00759-0
    DOI: 10.1007/s10898-019-00759-0
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    References listed on IDEAS

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    1. Felipe Viana & Raphael Haftka & Layne Watson, 2013. "Efficient global optimization algorithm assisted by multiple surrogate techniques," Journal of Global Optimization, Springer, vol. 56(2), pages 669-689, June.
    2. Juliane Müller & Christine Shoemaker, 2014. "Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems," Journal of Global Optimization, Springer, vol. 60(2), pages 123-144, October.
    3. Rommel Regis & Christine Shoemaker, 2005. "Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions," Journal of Global Optimization, Springer, vol. 31(1), pages 153-171, January.
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    Cited by:

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    2. Zhang, Jinhao & Gao, Liang & Xiao, Mi, 2020. "A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

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