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

Liu, Bo, Koziel, S and Zhang, Qingfu (2015) A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems. Journal of Computational Science, 12. pp. 28-37. ISSN 1877-7503

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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: Article
Keywords: Multi-fidelity, Multilevel, Variable fidelity, Surrogate-model-assisted evolutionary algorithm, Expensive optimization
Divisions: Applied Science, Computing and Engineering
Depositing User: Mr Stewart Milne
Date Deposited: 10 Jun 2016 12:59
Last Modified: 26 Apr 2018 13:30
URI: http://glyndwr.collections.crest.ac.uk/id/eprint/9269

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