| Peer-Reviewed

A Review on Surrogate-Based Global Optimization Methods for Computationally Expensive Functions

Received: 2 October 2019     Accepted: 21 October 2019     Published: 19 November 2019
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Abstract

The great computational burden caused by complicated and unknown analysis restricts the use of simulation-based optimization. In order to mitigate this challenge, surrogate-based global optimization methods have gained popularity for their capability in handling computationally expensive functions. This paper surveys the fundamental issues that arise in Surrogate-based Global Optimization (SBGO) from a practitioner’s perspective, including highlighting concepts, methods, techniques as well as engineering applications. To provide a comprehensive discussion on the issues involved, recent advances in design of experiments, surrogate modeling techniques, infill criteria and design space reduction are investigated. This review screens out nearly 130 references containing a lot of historical reviews on related research fields from about 500 publications in various subjects. Future challenges and research is also analyzed and discussed.

Published in Software Engineering (Volume 7, Issue 4)
DOI 10.11648/j.se.20190704.11
Page(s) 68-84
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Global Optimization, Surrogate Models, Review, Computationally Expensive Functions, Future Challenges

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    Pengcheng Ye. (2019). A Review on Surrogate-Based Global Optimization Methods for Computationally Expensive Functions. Software Engineering, 7(4), 68-84. https://doi.org/10.11648/j.se.20190704.11

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    Pengcheng Ye. A Review on Surrogate-Based Global Optimization Methods for Computationally Expensive Functions. Softw. Eng. 2019, 7(4), 68-84. doi: 10.11648/j.se.20190704.11

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    Pengcheng Ye. A Review on Surrogate-Based Global Optimization Methods for Computationally Expensive Functions. Softw Eng. 2019;7(4):68-84. doi: 10.11648/j.se.20190704.11

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  • @article{10.11648/j.se.20190704.11,
      author = {Pengcheng Ye},
      title = {A Review on Surrogate-Based Global Optimization Methods for Computationally Expensive Functions},
      journal = {Software Engineering},
      volume = {7},
      number = {4},
      pages = {68-84},
      doi = {10.11648/j.se.20190704.11},
      url = {https://doi.org/10.11648/j.se.20190704.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20190704.11},
      abstract = {The great computational burden caused by complicated and unknown analysis restricts the use of simulation-based optimization. In order to mitigate this challenge, surrogate-based global optimization methods have gained popularity for their capability in handling computationally expensive functions. This paper surveys the fundamental issues that arise in Surrogate-based Global Optimization (SBGO) from a practitioner’s perspective, including highlighting concepts, methods, techniques as well as engineering applications. To provide a comprehensive discussion on the issues involved, recent advances in design of experiments, surrogate modeling techniques, infill criteria and design space reduction are investigated. This review screens out nearly 130 references containing a lot of historical reviews on related research fields from about 500 publications in various subjects. Future challenges and research is also analyzed and discussed.},
     year = {2019}
    }
    

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    T1  - A Review on Surrogate-Based Global Optimization Methods for Computationally Expensive Functions
    AU  - Pengcheng Ye
    Y1  - 2019/11/19
    PY  - 2019
    N1  - https://doi.org/10.11648/j.se.20190704.11
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    T2  - Software Engineering
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    AB  - The great computational burden caused by complicated and unknown analysis restricts the use of simulation-based optimization. In order to mitigate this challenge, surrogate-based global optimization methods have gained popularity for their capability in handling computationally expensive functions. This paper surveys the fundamental issues that arise in Surrogate-based Global Optimization (SBGO) from a practitioner’s perspective, including highlighting concepts, methods, techniques as well as engineering applications. To provide a comprehensive discussion on the issues involved, recent advances in design of experiments, surrogate modeling techniques, infill criteria and design space reduction are investigated. This review screens out nearly 130 references containing a lot of historical reviews on related research fields from about 500 publications in various subjects. Future challenges and research is also analyzed and discussed.
    VL  - 7
    IS  - 4
    ER  - 

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  • School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China

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