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

Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems
Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems
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 Structural 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 frameworks 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.
3540229027
167
307-332
Springer
Ong, Yew Soon
3e7a6a91-6eab-4ca6-81c5-c9f3ee20e2fb
Nair, P.B.
d4d61705-bc97-478e-9e11-bcef6683afe7
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Wong, K.W.
08582202-8c7f-46b5-bd7e-13e3ddb9afb0
Jin, Yaochu
Ong, Yew Soon
3e7a6a91-6eab-4ca6-81c5-c9f3ee20e2fb
Nair, P.B.
d4d61705-bc97-478e-9e11-bcef6683afe7
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Wong, K.W.
08582202-8c7f-46b5-bd7e-13e3ddb9afb0
Jin, Yaochu

Ong, Yew Soon, Nair, P.B., Keane, A.J. and Wong, K.W. (2004) Surrogate-assisted evolutionary optimization frameworks for high-fidelity engineering design problems. In, Jin, Yaochu (ed.) Knowledge Incorporation in Evolutionary Computation. (Studies in Fuzziness and Soft Computing, 167) Springer, pp. 307-332.

Record type: Book Section

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 Structural 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 frameworks 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.

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Published date: 2004

Identifiers

Local EPrints ID: 22921
URI: http://eprints.soton.ac.uk/id/eprint/22921
ISBN: 3540229027
PURE UUID: 272655bb-74d0-4e58-b979-8d5b118fbb07
ORCID for A.J. Keane: ORCID iD orcid.org/0000-0001-7993-1569

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Date deposited: 24 Mar 2006
Last modified: 16 Mar 2024 02:53

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Contributors

Author: Yew Soon Ong
Author: P.B. Nair
Author: A.J. Keane ORCID iD
Author: K.W. Wong
Editor: Yaochu Jin

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