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

Sóbester, András and Keane, Andy J. (2002) Empirical comparisons of gradient-based methods on an engine-inlet shape optimization problem In Proceedings of the 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. American Institute of Aeronautics and Astronautics. 11 pp.

Record type: Conference or Workshop Item (Paper)


With the development of increasingly sophisticated adjoint flow-solvers capable of providing objective function gradients at reasonable computational costs, modern deterministic gradient-based search methods have come to be regarded as amongst 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 jet-engine 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
Additional Information: CD-ROM
Venue - Dates: 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 2002-09-04 - 2002-09-06


Local EPrints ID: 23052
PURE UUID: 71fb202a-9e79-4f4c-9665-1bcea5053c2c
ORCID for András Sóbester: ORCID iD

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Date deposited: 07 Mar 2007
Last modified: 17 Jul 2017 16:19

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