A framework for memetic optimization using variable global and local surrogate models

Authors:
Yoel Tenne;S. W. Armfield
Affiliations:
The University of Sydney, School of Aerospace, Mechanical and Mechatronic Engineering, Sydney, Australia;The University of Sydney, School of Aerospace, Mechanical and Mechatronic Engineering, Sydney, Australia
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Year:
2009

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Abstract

We propose a framework of memetic optimization using variable global and local surrogate-models for optimization of expensive functions. The framework employs the trust-region approach but replaces the quadratic models with the more general RBF ones. It makes an extensive use of accuracy assessment to select the models used and to improve them if necessary. It also employs several efficient and stable numerical methods to improve its performance. Rigorous performance analysis shows the proposed framework significantly outperforms several existing surrogate-assisted evolutionary algorithms.