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Empirical comparison of gradient-based methods on an engine-inlet shape optimization problem

Empirical comparison of gradient-based methods on an engine-inlet shape optimization problem
Empirical comparison of gradient-based methods on an engine-inlet shape optimization problem
With the development of increasingly sophisticated adjoint flow-solvers capable of providing objective function gradients at reasonable computational costs, modern deterministic gradientbased search methods have come to be regarded as the most powerful tools in aerodynamic shape optimization and MDO problems. However, their performance can be disappointing when the objective function landscape features multiple local optima, long valleys, noise or discontinuities. Equally, stochastic global explorers, such as Genetic Algorithms (GAs), while less affected by these problems, are relatively slow to converge. In this paper we propose GLOSSY (Global/Local Search Strategy), a generic hybrid approach, which combines a global exploration method with gradient-based exploitation. We analyze the performance of two optimizers based on the GLOSSY framework (fusing a GA with a quasi-Newton local search method) and we show through a set of comparative tests that on the moderately noisy objective landscape of a jetengine inlet shape optimization problem the hybrid outperforms both of its components used individually. We also look at the issue of what global / local search effort ratio gives the hybrid the best performance.
Sobester, A.
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Keane, A.J.
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Sobester, A.
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def

Sobester, A. and Keane, A.J. (2002) Empirical comparison of gradient-based methods on an engine-inlet shape optimization problem. 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, Georgia. 03 - 05 Sep 2002. 11 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

With the development of increasingly sophisticated adjoint flow-solvers capable of providing objective function gradients at reasonable computational costs, modern deterministic gradientbased search methods have come to be regarded as the most powerful tools in aerodynamic shape optimization and MDO problems. However, their performance can be disappointing when the objective function landscape features multiple local optima, long valleys, noise or discontinuities. Equally, stochastic global explorers, such as Genetic Algorithms (GAs), while less affected by these problems, are relatively slow to converge. In this paper we propose GLOSSY (Global/Local Search Strategy), a generic hybrid approach, which combines a global exploration method with gradient-based exploitation. We analyze the performance of two optimizers based on the GLOSSY framework (fusing a GA with a quasi-Newton local search method) and we show through a set of comparative tests that on the moderately noisy objective landscape of a jetengine inlet shape optimization problem the hybrid outperforms both of its components used individually. We also look at the issue of what global / local search effort ratio gives the hybrid the best performance.

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Published date: 2002
Venue - Dates: 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, Georgia, 2002-09-03 - 2002-09-05

Identifiers

Local EPrints ID: 22082
URI: http://eprints.soton.ac.uk/id/eprint/22082
PURE UUID: 9ac464e4-2af7-4d86-9de2-4be9397f9a71
ORCID for A. Sobester: ORCID iD orcid.org/0000-0002-8997-4375

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Date deposited: 05 Jun 2006
Last modified: 23 Jul 2020 00:29

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