Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems

Ong, Y.S., Nair, P.B., Keane, A.J. and Wong, K.W. (2004) Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems. In: Yaochu, J., (ed.) Knowledge Incorporation in Evolutionary Computing. Springer Berlin, Heidelberg.

Abstract

Over the last decade, Evolutionary Algorithms (EAs) have emerged as a powerful paradigm for global optimization of multimodal functions. More recently, there has been significant interest in applying EAs to engineering design problems. However, in many complex engineering design problems where high-fidelity analysis models are used, each function evaluation may require a Computational Structureal Mechanics (CSM), Computational Fluid Dynamics (CFD) or Computational Electro-Magnetics (CEM) simulation costing minutes to hours of supercomputer time. Since EAs typically require thousands of function evaluations to locate a near optimal solution, the use of EAs often becomes computationally prohibitive for this class of problems. In this chapter, we present fraemworks that employ surrogate models for solving computationally expensive optimization problems on a limited computational budget. In particular, the key factors responsible for the success of these frameworks are discussed. Experimental results obtained on benchmark test functions and real-world complex design problems are presented.

Item Type:	Book Chapter
Murdoch Affiliation(s):	School of Information Technology
Publisher:	Springer Berlin
Copyright:	© Springer-Verlag Berlin Heidelberg 2005
URI:	http://researchrepository.murdoch.edu.au/id/eprint/1035

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