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 |
Global Optimization, Surrogate Models, Review, Computationally Expensive Functions, Future Challenges
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APA Style
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
ACS Style
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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Download@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}
}
TY - JOUR 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 DO - 10.11648/j.se.20190704.11 T2 - Software Engineering JF - Software Engineering JO - Software Engineering SP - 68 EP - 84 PB - Science Publishing Group SN - 2376-8037 UR - https://doi.org/10.11648/j.se.20190704.11 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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