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Design space reduction in optimization using generative topographic mapping

Design space reduction in optimization using generative topographic mapping
Design space reduction in optimization using generative topographic mapping
Dimension reduction in design optimization is an extensively researched area. The need arises in design problems dealing with very high dimensions, which increase the computational burden of the design process because the sample space required for the design search varies exponentially with the dimensions. This work describes the application of a latent variable method called Generative Topographic Mapping (GTM) in dimension reduction of a data set by transformation into a low-dimensional latent space. The attraction it presents is that the variables are not removed, but only transformed and hence there is no risk of missing out on information relating to all the variables. The method has been tested on the Branin test function initially and then on an aircraft wing weight problem. Ongoing work involves finding a suitable update strategy for adding infill points to the trained GTM in order to converge to the global optimum effectively. Three update methods tested on GTM so far are discussed.
dimension reduction, gtm, response-surface, global optimization, exploration, weighted lower bound
Viswanath, Asha
385f876f-ce34-4973-be3a-e6a0339fb48f
Forrester, Alexander
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Keane, Andy
26d7fa33-5415-4910-89d8-fb3620413def
Viswanath, Asha
385f876f-ce34-4973-be3a-e6a0339fb48f
Forrester, Alexander
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Keane, Andy
26d7fa33-5415-4910-89d8-fb3620413def

Viswanath, Asha, Forrester, Alexander and Keane, Andy (2009) Design space reduction in optimization using generative topographic mapping. 8th World Congress on Structural and Multidisciplinary Optimization, Lisbon, Portugal. 01 - 08 Jun 2009. 10 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Dimension reduction in design optimization is an extensively researched area. The need arises in design problems dealing with very high dimensions, which increase the computational burden of the design process because the sample space required for the design search varies exponentially with the dimensions. This work describes the application of a latent variable method called Generative Topographic Mapping (GTM) in dimension reduction of a data set by transformation into a low-dimensional latent space. The attraction it presents is that the variables are not removed, but only transformed and hence there is no risk of missing out on information relating to all the variables. The method has been tested on the Branin test function initially and then on an aircraft wing weight problem. Ongoing work involves finding a suitable update strategy for adding infill points to the trained GTM in order to converge to the global optimum effectively. Three update methods tested on GTM so far are discussed.

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More information

Published date: June 2009
Venue - Dates: 8th World Congress on Structural and Multidisciplinary Optimization, Lisbon, Portugal, 2009-06-01 - 2009-06-08
Keywords: dimension reduction, gtm, response-surface, global optimization, exploration, weighted lower bound

Identifiers

Local EPrints ID: 69770
URI: http://eprints.soton.ac.uk/id/eprint/69770
PURE UUID: ea8ac0b4-32a4-4fe7-9e49-b678b89baa75
ORCID for Andy Keane: ORCID iD orcid.org/0000-0001-7993-1569

Catalogue record

Date deposited: 02 Dec 2009
Last modified: 14 Mar 2024 02:39

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Contributors

Author: Asha Viswanath
Author: Andy Keane ORCID iD

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