Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147970
Title: Combining global and local surrogate models to accelerate evolutionary optimization
Authors: Zhou, Zongzhao
Ong, Yew-Soon
Nair, Prasanth B.
Keane, Andy J.
Lum, Kai Yew
Keywords: Engineering::Computer science and engineering
Issue Date: 2007
Source: Zhou, Z., Ong, Y., Nair, P. B., Keane, A. J. & Lum, K. Y. (2007). Combining global and local surrogate models to accelerate evolutionary optimization. IEEE Transactions On Systems, Man and Cybernetics Part C: Applications and Reviews, 37(1), 66-76. https://dx.doi.org/10.1109/TSMCC.2005.855506
Project: CE-SUG 3/03
Journal: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Abstract: In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems. The proposed framework uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive objective functions during evolutionary search. At the first level, the framework employs a data-parallel Gaussian process based global surrogate model to filter the evolutionary algorithm (EA) population of promising individuals. Subsequently, these potential individuals undergo a memetic search in the form of Lamarckian learning at the second level. The Lamarckian evolution involves a trust-region enabled gradient-based search strategy that employs radial basis function local surrogate models to accelerate convergence. Numerical results are presented on a series of benchmark test functions and on an aerodynamic shape design problem. The results obtained suggest that the proposed optimization framework converges to good designs on a limited computational budget. Furthermore, it is shown that the new algorithm gives significant savings in computational cost when compared to the traditional evolutionary algorithm and other surrogate assisted optimization frameworks.
URI: https://hdl.handle.net/10356/147970
ISSN: 1094-6977
DOI: 10.1109/TSMCC.2005.855506
Rights: © 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TSMCC.2005.855506.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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