A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems

Liu, B. , Koziel, S. and Zhang, Q. (2016) A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems. Journal of Computational Science, 12, pp. 28-37. (doi: 10.1016/j.jocs.2015.11.004)

Abstract

Integrating data-driven surrogate models and simulation models of different accuracies (or fidelities) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple fidelities in global optimization is a major challenge. To address it, the two major contributions of this paper include: (1) development of a new multi-fidelity surrogate-model-based optimization framework, which substantially improves reliability and efficiency of optimization compared to many existing methods, and (2) development of a data mining method to address the discrepancy between the low- and high-fidelity simulation models. A new efficient global optimization method is then proposed, referred to as multi-fidelity Gaussian process and radial basis function-model-assisted memetic differential evolution. Its advantages are verified by mathematical benchmark problems and a real-world antenna design automation problem.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Professor Bo
• Authors: Liu, B., Koziel, S., and Zhang, Q.
• College/School: College of Science and Engineering > School of Engineering
• Journal Name: Journal of Computational Science
• Publisher: Elsevier
• ISSN: 1877-7503
• ISSN (Online): 1877-7511
• Published Online: 27 November 2015