Please use this identifier to cite or link to this item:
|Title:||Combining global and local surrogate models to accelerate evolutionary optimization||Authors:||Zhou, Zongzhao
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|
Files in This Item:
|Combining global and local surrogate models to accelerate evolutionary optimization.pdf||220.2 kB||Adobe PDF||View/Open|
Updated on May 19, 2022
Updated on May 19, 2022
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.