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

Liu, B and Koziel, S and Zhang, Q (2016) 'A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems.' Journal of Computational Science, 12. 28 - 37. ISSN 1877-7503

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Abstract

© 2015. 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
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 04 Dec 2015 15:21
Last Modified: 15 Nov 2018 18:33
URI: http://repository.essex.ac.uk/id/eprint/15604

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