Partial least squares Kriging model assisted efficient global optimization method

被引：0
|
作者：
Peng X. [1 ]
Ma Y. [1 ]
Lin C. [1 ]
机构：
[1] School of Economics and Management, Nanjing University of Science and Technology, Nanjing
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2023年 / 29卷 / 07期
关键词：
efficient global optimization method; Kriging model; partial least squares expected improvement criterion; partial least squares kernel function; probability of feasibility;
D O I：
10.13196/j.cims.2023.07.020
中图分类号：
学科分类号：
摘要：
Considering the curse of dimensionality and low optimization efficiency caused by hypcr-paramctcrs in expensive constrained optimization problems > a new partial least squares Kriging model assisted efficient global optimization method was proposed. The efficiency of building Kriging model was improved by using the partial least squares kernel function , and two partial least squares weighted expectation infill criteria were involved to realize model adaptive adjustment and efficient global optimization. Test functions and engineering examples showed that the proposed method could decrease the calculation in hypcr-paramctcr and improve the efficiency of solving expensive constrained optimization problems. Especially in high-dimensional problems∗ the proposed method could obtain superior solutions in terms of convergence speed∗ robustness and accuracy. © 2023 CIMS. All rights reserved.
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页码：2376 / 2384
页数：8
相关论文
共 29 条
  • [1] OUYANG L H, ZHENG W, ZHU Y G, Et al., An interval probability-based FMEA model for risk assessment: A real world case, Quality and Reliability Engineering International, 36, 1, pp. 125-143, (2020)
  • [2] FORRESTER A, SOBESTER A, KEANE A., Engineering design via surrogate modelling: A practical guide, (2008)
  • [3] LIN Chenglong, MA Yizhong, XIAO Tianli, Expensive constraints parallel surrogate-based optimization algorithm based on mean improvement strategy, Control Theory o- Applications, 38, 6, pp. 707-718, (2021)
  • [4] JAKOBSSONS, PATRIKSSON M, RUDHOLM J, Et al., A method for simulation based optimization using radial basis functions, Optimization & Engineering, 11, pp. 501-532, (2010)
  • [5] HAN Zhonghua, Kriging Surrogate model and its application to design optimization: A review of recent progress, Acta Aeronautica et AstronauticaSinica, 37, 11, pp. 3197-3225, (2016)
  • [6] WESTERMANN P, EVINS R., Surrogate modelling for sustainable building design-A review, Energy and Buildings, 198, pp. 170-186, (2019)
  • [7] KENNEDY M, CO'HAGAN A., Predicting the output from a complex computer code when fast approximations are available, Biemetrika, 87, 1, pp. 1-13, (2000)
  • [8] ANKENMAN B, NELSON B L, ST AUM J., Stochastic Kriging for simulation metamodeling, Operations Research, 58, 2, pp. 371-382, (2010)
  • [9] ZHANG Y, YAO W, CHEN X, Et al., A penalized blind likelihood Kriging method for surrogate modeling, Structural and Multidisciplinary Optimization, 61, pp. 457-474, (2020)
  • [10] LONGTeng, LIU Jian, WANG G G, Et al., Dscuss on approximate optimization strategies using design of computer experiments and metamodels for flight vehicle design[J], Journal of Mechanical Engineering, 52, 14, pp. 79-105, (2016)
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