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A kriging-based adaptive global optimization method with generalized expected improvement and its application in numerical simulation and crop evapotranspiration

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  • Li, Yaohui
  • Shi, Junjun
  • Cen, Hui
  • Shen, Jingfang
  • Chao, Yanpu

Abstract

The generalized effective global optimization (EGO) method based on Kriging model can sequentially solve the expensive black-box problems. However, it can only obtain one sampling point in a cycle, which will result in more time spent on expensive function evaluations and affect the global convergence. To this end, A Kriging-based adaptive global optimization method with generalized expected improvement (KAGO-GEI) is proposed. It divides the enhanced generalized expected improvement (GEI) criterion which recursively changes in the iterative process into double objectives, and then uses multi-objective PSO method to optimize the two objectives to produce the Pareto frontier. Further, more valuable sampling points from Pareto frontier are screened and corrected as the expensive-evaluation points for updating Kriging model. Test results on eighteen benchmark functions and crop evapotranspiration calculation example show that the proposed method is superior to other classical optimization methods in terms of convergence and accuracy in most cases.

Suggested Citation

  • Li, Yaohui & Shi, Junjun & Cen, Hui & Shen, Jingfang & Chao, Yanpu, 2021. "A kriging-based adaptive global optimization method with generalized expected improvement and its application in numerical simulation and crop evapotranspiration," Agricultural Water Management, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:agiwat:v:245:y:2021:i:c:s0378377420321703
    DOI: 10.1016/j.agwat.2020.106623
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    References listed on IDEAS

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    1. Hossein Tabari, 2010. "Evaluation of Reference Crop Evapotranspiration Equations in Various Climates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2311-2337, August.
    2. Yaohui Li & Yizhong Wu & Jianjun Zhao & Liping Chen, 2017. "A Kriging-based constrained global optimization algorithm for expensive black-box functions with infeasible initial points," Journal of Global Optimization, Springer, vol. 67(1), pages 343-366, January.
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    Cited by:

    1. Xiaodong Song & Mingyang Li & Zhitao Li & Fang Liu, 2021. "Global Optimization Algorithm Based on Kriging Using Multi-Point Infill Sampling Criterion and Its Application in Transportation System," Sustainability, MDPI, vol. 13(19), pages 1-17, September.
    2. Li Lu & Yizhong Wu & Qi Zhang & Zhehao Xia & Ping Qiao, 2024. "A response band-based method for time-dependent reliability-based robust design optimization," Journal of Risk and Reliability, , vol. 238(3), pages 559-577, June.
    3. Li Lu & Yizhong Wu & Qi Zhang & Ping Qiao, 2023. "A Transformation-Based Improved Kriging Method for the Black Box Problem in Reliability-Based Design Optimization," Mathematics, MDPI, vol. 11(1), pages 1-19, January.
    4. Yaohui Li & Junjun Shi & Zhifeng Yin & Jingfang Shen & Yizhong Wu & Shuting Wang, 2021. "An Improved High-Dimensional Kriging Surrogate Modeling Method through Principal Component Dimension Reduction," Mathematics, MDPI, vol. 9(16), pages 1-18, August.

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