IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v73y2019i3d10.1007_s10898-018-0716-0.html
   My bibliography  Save this article

Conditional optimization of a noisy function using a kriging metamodel

Author

Listed:
  • Diariétou Sambakhé

    (Centre d’étude régional pour l’amélioration de l’adaptation à la sécheresse
    CIRAD, UMR AGAP
    AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro
    IMAG, Univ Montpellier, CNRS)

  • Lauriane Rouan

    (CIRAD, UMR AGAP
    AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro)

  • Jean-Noël Bacro

    (IMAG, Univ Montpellier, CNRS)

  • Eric Gozé

    (CIRAD, UPR AIDA
    AIDA, Univ Montpellier, CIRAD)

Abstract

The efficient global optimization method is popular for the global optimization of computer-intensive black-box functions. Extensions exist, either for the optimization of noisy functions, or for the conditional optimization of deterministic functions, i.e. the search for the values of a subset of parameters that optimize the function conditionally to the values taken by another subset, which are fixed. A metaphor for conditional optimization is the search for a crest line. No method has yet been developed for the conditional optimization of noisy functions: this is what we propose in this article. Testing this new method on test functions showed that, in the case of a high level of noise on the function, the PEQI criterion that we propose is better than the PEI criterion usually implemented in such a situation.

Suggested Citation

  • Diariétou Sambakhé & Lauriane Rouan & Jean-Noël Bacro & Eric Gozé, 2019. "Conditional optimization of a noisy function using a kriging metamodel," Journal of Global Optimization, Springer, vol. 73(3), pages 615-636, March.
    • Handle: RePEc:spr:jglopt:v:73:y:2019:i:3:d:10.1007_s10898-018-0716-0
    DOI: 10.1007/s10898-018-0716-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10898-018-0716-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10898-018-0716-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. D. Huang & T. Allen & W. Notz & N. Zeng, 2006. "Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models," Journal of Global Optimization, Springer, vol. 34(3), pages 441-466, March.
    2. Jones, Peter G. & Thornton, Philip K., 2013. "Generating downscaled weather data from a suite of climate models for agricultural modelling applications," Agricultural Systems, Elsevier, vol. 114(C), pages 1-5.
    3. Picheny, Victor & Ginsbourger, David, 2014. "Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1035-1053.
    4. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ehsan Mehdad & Jack P. C. Kleijnen, 2018. "Efficient global optimisation for black-box simulation via sequential intrinsic Kriging," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(11), pages 1725-1737, November.
    2. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging," Other publications TiSEM 8fa8d96f-a086-4c4b-88ab-9, Tilburg University, School of Economics and Management.
    3. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
    4. Mickaël Binois & David Ginsbourger & Olivier Roustant, 2020. "On the choice of the low-dimensional domain for global optimization via random embeddings," Journal of Global Optimization, Springer, vol. 76(1), pages 69-90, January.
    5. Jalali, Hamed & Van Nieuwenhuyse, Inneke & Picheny, Victor, 2017. "Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise," European Journal of Operational Research, Elsevier, vol. 261(1), pages 279-301.
    6. Youssef Diouane & Victor Picheny & Rodolophe Le Riche & Alexandre Scotto Di Perrotolo, 2023. "TREGO: a trust-region framework for efficient global optimization," Journal of Global Optimization, Springer, vol. 86(1), pages 1-23, May.
    7. Picheny, Victor & Ginsbourger, David, 2014. "Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1035-1053.
    8. Jonathan E. Suk & Kristie L. Ebi & David Vose & Willy Wint & Neil Alexander & Koen Mintiens & Jan C. Semenza, 2014. "Indicators for Tracking European Vulnerabilities to the Risks of Infectious Disease Transmission due to Climate Change," IJERPH, MDPI, vol. 11(2), pages 1-18, February.
    9. 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.
    10. Bell, N.O. & Bilbao, J.I. & Kay, M. & Sproul, A.B., 2022. "Future climate scenarios and their impact on heating, ventilation and air-conditioning system design and performance for commercial buildings for 2050," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    11. Songhao Wang & Szu Hui Ng & William Benjamin Haskell, 2022. "A Multilevel Simulation Optimization Approach for Quantile Functions," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 569-585, January.
    12. Habtemariam, Lemlem Teklegiorgis & Abate Kassa, Getachew & Gandorfer, Markus, 2017. "Impact of climate change on farms in smallholder farming systems: Yield impacts, economic implications and distributional effects," Agricultural Systems, Elsevier, vol. 152(C), pages 58-66.
    13. Biewen, Martin & Kugler, Philipp, 2021. "Two-stage least squares random forests with an application to Angrist and Evans (1998)," Economics Letters, Elsevier, vol. 204(C).
    14. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    15. Rashid, Muhammad Adil & Jabloun, Mohamed & Andersen, Mathias Neumann & Zhang, Xiying & Olesen, Jørgen Eivind, 2019. "Climate change is expected to increase yield and water use efficiency of wheat in the North China Plain," Agricultural Water Management, Elsevier, vol. 222(C), pages 193-203.
    16. Olgun Aydin & Bartłomiej Igliński & Krzysztof Krukowski & Marek Siemiński, 2022. "Analyzing Wind Energy Potential Using Efficient Global Optimization: A Case Study for the City Gdańsk in Poland," Energies, MDPI, vol. 15(9), pages 1-22, April.
    17. Yuan, Jun & Shi, Xunpeng & He, Junliang, 2024. "LNG market liberalization and LNG transportation: Evaluation based on fleet size and composition model," Applied Energy, Elsevier, vol. 358(C).
    18. Qi Fan & Jiaqiao Hu, 2018. "Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 677-693, November.
    19. Donghun Lee, 2022. "Knowledge Gradient: Capturing Value of Information in Iterative Decisions under Uncertainty," Mathematics, MDPI, vol. 10(23), pages 1-20, November.
    20. Xinyu Dong & Peng Yuan & Yonghui Song & Wenxuan Yi, 2021. "Optimizing Green-Gray Infrastructure for Non-Point Source Pollution Control under Future Uncertainties," IJERPH, MDPI, vol. 18(14), pages 1-16, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jglopt:v:73:y:2019:i:3:d:10.1007_s10898-018-0716-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.