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2007-Memetic algorithm using multi-surrogates for computationally expensive optimization problems.pdf

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2007-Memetic algorithm using multi-surrogates for computationally expensive optimization problems

Soft Comput (2007) 11:957–971 DOI 10.1007/s00500-006-0145-8 ORIGINAL PAPER Memetic algorithm using multi-surrogates for computationally expensive optimization problems Zongzhao Zhou · Yew Soon Ong · Meng Hiot Lim · Bu Sung Lee Published online: 16 December 2006 ? Springer-Verlag 2006 Abstract In this paper, we present a multi-surrogates assisted memetic algorithm for solving optimization problems with computationally expensive fitness func- tions. The essential backbone of our framework is an evolutionary algorithm coupled with a local search solver that employs multi-surrogate in the spirit of Lamarckian learning. Inspired by the notion of ‘blessing and curse of uncertainty’ in approximation models, we combine regression and exact interpolating surrogate models in the evolutionary search. Empirical results are presented for a series of commonly used benchmark problems to demonstrate that the proposed framework converges to good solution quality more efficiently than the standard genetic algorithm, memetic algorithm and surrogate-assisted memetic algorithms. Keywords Evolutionary optimization ·Memetic algorithm · Surrogate model · Radial basis function · Polynomial regression Z. Zhou · Y. S. Ong (B) · B. S. Lee School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore e-mail: ebslee@ntu.edu.sg Z. Zhou e-mail: zhou0018@ntu.edu.sg Y. S. Ong e-mail: asysong@ntu.edu.sg M. H. Lim School of Electrical and Electronic Engineering, Block S1, Nanyang Technological University, Singapore 639798, Singapore e-mail: emhlim@ntu.edu.sg 1 Introduction A continuing trend in science and engineering is the use of increasingly accurate simulation codes in the design and analysis process so as to produce evenmore reliable and high quality products. Modern computational struc- turalmechanics (CSM),Computational electro-magnet- ics (CEM) and computational fluid dynamics (CFD) analysis represent some of the recent technologies that now play a central role