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

摘要

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.