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Global Optimization Algorithm Based on Kriging Using Multi-Point Infill Sampling Criterion and Its Application in Transportation System

Author

Listed:
  • Xiaodong Song

    (Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Mingyang Li

    (Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Zhitao Li

    (Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Fang Liu

    (School of Transportation Engineering, Changsha University of Science and Technology, Changsha 410205, China)

Abstract

Public traffic has a great influence, especially with the background of COVID-19. Solving simulation-based optimization (SO) problem is efficient to study how to improve the performance of public traffic. Global optimization based on Kriging (KGO) is an efficient method for SO; to this end, this paper proposes a Kriging-based global optimization using multi-point infill sampling criterion. This method uses an infill sampling criterion which obtains multiple new design points to update the Kriging model through solving the constructed multi-objective optimization problem in each iteration. Then, the typical low-dimensional and high-dimensional nonlinear functions, and a SO based on 445 bus line in Beijing city, are employed to test the performance of our algorithm. Moreover, compared with the KGO based on the famous single-point expected improvement (EI) criterion and the particle swarm algorithm (PSO), our method can obtain better solutions in the same amount or less time. Therefore, the proposed algorithm expresses better optimization performance, and may be more suitable for solving the tricky and expensive simulation problems in real-world traffic problems.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10645-:d:642820
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    References listed on IDEAS

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    1. Zhiwei Feng & Qingbin Zhang & Qingfu Zhang & Qiangang Tang & Tao Yang & Yang Ma, 2015. "A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization," Journal of Global Optimization, Springer, vol. 61(4), pages 677-694, April.
    2. Jack Kleijnen & Wim Beers & Inneke Nieuwenhuyse, 2012. "Expected improvement in efficient global optimization through bootstrapped kriging," Journal of Global Optimization, Springer, vol. 54(1), pages 59-73, September.
    3. Kroetz, H.M. & Moustapha, M. & Beck, A.T. & Sudret, B., 2020. "A Two-Level Kriging-Based Approach with Active Learning for Solving Time-Variant Risk Optimization Problems," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    4. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
    5. Pourya Pourhejazy & Oh Kyoung Kwon, 2016. "The New Generation of Operations Research Methods in Supply Chain Optimization: A Review," Sustainability, MDPI, vol. 8(10), pages 1-23, October.
    6. Ribaud, Mélina & Blanchet-Scalliet, Christophette & Helbert, Céline & Gillot, Frédéric, 2020. "Robust optimization: A kriging-based multi-objective optimization approach," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    7. 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).
    Full references (including those not matched with items on IDEAS)

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