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Evolutionary optimization of computationally expensive problems via surrogate modeling

Evolutionary optimization of computationally expensive problems via surrogate modeling
Evolutionary optimization of computationally expensive problems via surrogate modeling
We present a parallel evolutionary optimization algorithm that leverages surrogate models for solving computationally expensive design problems with general constraints, on a limited computational budget. The essential backbone of our framework is an evolutionary algorithm coupled with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning. We employ a trust-region approach for interleaving use of exact models for the objective and constraint functions with computationally cheap surrogatemodels during local search. In contrast to earlier work, we construct local surrogate models using radial basis functions motivated by the principle of transductive inference. Further, the present approach retains the intrinsic parallelism of evolutionary algorithms and can hence be readily implemented on grid computing infrastructures. Experimental results are presented for some benchmark test functions and an aerodynamic wing design problem to demonstrate that our algorithm converges to good designs on a limited computational budget.
0001-1452
687-696
Ong, Yew S.
d25bbd61-6dee-464f-8147-6f7b21bf9237
Nair, Prasanth B.
d4d61705-bc97-478e-9e11-bcef6683afe7
Keane, Andrew J.
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Ong, Yew S.
d25bbd61-6dee-464f-8147-6f7b21bf9237
Nair, Prasanth B.
d4d61705-bc97-478e-9e11-bcef6683afe7
Keane, Andrew J.
26d7fa33-5415-4910-89d8-fb3620413def

Ong, Yew S., Nair, Prasanth B. and Keane, Andrew J. (2003) Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA Journal, 41 (4), 687-696.

Record type: Article

Abstract

We present a parallel evolutionary optimization algorithm that leverages surrogate models for solving computationally expensive design problems with general constraints, on a limited computational budget. The essential backbone of our framework is an evolutionary algorithm coupled with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning. We employ a trust-region approach for interleaving use of exact models for the objective and constraint functions with computationally cheap surrogatemodels during local search. In contrast to earlier work, we construct local surrogate models using radial basis functions motivated by the principle of transductive inference. Further, the present approach retains the intrinsic parallelism of evolutionary algorithms and can hence be readily implemented on grid computing infrastructures. Experimental results are presented for some benchmark test functions and an aerodynamic wing design problem to demonstrate that our algorithm converges to good designs on a limited computational budget.

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Published date: 2003

Identifiers

Local EPrints ID: 22421
URI: http://eprints.soton.ac.uk/id/eprint/22421
ISSN: 0001-1452
PURE UUID: 8e7d3136-a152-41dd-9e43-0015e7a71435
ORCID for Andrew J. Keane: ORCID iD orcid.org/0000-0001-7993-1569

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Date deposited: 22 Mar 2006
Last modified: 16 Mar 2024 02:53

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Contributors

Author: Yew S. Ong
Author: Prasanth B. Nair
Author: Andrew J. Keane ORCID iD

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