Volume 7, Issue 4, December 2019, Page: 68-84
A Review on Surrogate-Based Global Optimization Methods for Computationally Expensive Functions
Pengcheng Ye, School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China; Key Laboratory for Unmanned Underwater Vehicle, Northwestern Polytechnical University, Xi’an, China
Received: Oct. 2, 2019;       Accepted: Oct. 21, 2019;       Published: Nov. 19, 2019
DOI: 10.11648/j.se.20190704.11      View  33      Downloads  19
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.
Keywords
Global Optimization, Surrogate Models, Review, Computationally Expensive Functions, Future Challenges
To cite this article
Pengcheng Ye, A Review on Surrogate-Based Global Optimization Methods for Computationally Expensive Functions, Software Engineering. Vol. 7, No. 4, 2019, pp. 68-84. doi: 10.11648/j.se.20190704.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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