Combining approximation concepts with genetic algorithm-based structural optimization procedures
Combining approximation concepts with genetic algorithm-based structural optimization procedures
This paper presents an approach for combining approximation models with genetic algorithm-based design optimization procedures. An important objective here is to develop an approach which empirically ensures that the GA converges asymptotically to the optima of the original problem using a limited number of exact analysis. It is shown that this problem may be posed as a dynamic optimization problem, wherein the fitness function changes over successive generations. Criteria for selecting the design points where exact analysis should be carried out are proposed based on observations on the steady-state behaviour of simple GAs. Guidelines based on trust region methods are presented for controlling the generation delay before the approximation model is updated. An adaptive selection operator is developed to efficiently navigate through such changing and uncertain fitness landscapes. Results are presented for the optimal design problem of a 10 bar truss structure. It is shown that, using the present approach, the number of exact analysis required to reach the optima of the original problem can be reduced by more than 97%.
1741-1751
American Institute of Aeronautics and Astronautics
Nair, P.B.
da7138d7-da7f-45af-887b-acc1d0e77a6f
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Shimpi, R.P.
b9e4ef7f-889e-4a07-be42-865f663c8ee7
1998
Nair, P.B.
da7138d7-da7f-45af-887b-acc1d0e77a6f
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Shimpi, R.P.
b9e4ef7f-889e-4a07-be42-865f663c8ee7
Nair, P.B., Keane, A.J. and Shimpi, R.P.
(1998)
Combining approximation concepts with genetic algorithm-based structural optimization procedures.
In 39th AIAA/ASME/ASCE/AHS/ACS Structures, Structural Dynamics and Materials Conference and Exhibition. Collection of technical papers, Pt.2.
American Institute of Aeronautics and Astronautics.
.
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Conference or Workshop Item
(Paper)
Abstract
This paper presents an approach for combining approximation models with genetic algorithm-based design optimization procedures. An important objective here is to develop an approach which empirically ensures that the GA converges asymptotically to the optima of the original problem using a limited number of exact analysis. It is shown that this problem may be posed as a dynamic optimization problem, wherein the fitness function changes over successive generations. Criteria for selecting the design points where exact analysis should be carried out are proposed based on observations on the steady-state behaviour of simple GAs. Guidelines based on trust region methods are presented for controlling the generation delay before the approximation model is updated. An adaptive selection operator is developed to efficiently navigate through such changing and uncertain fitness landscapes. Results are presented for the optimal design problem of a 10 bar truss structure. It is shown that, using the present approach, the number of exact analysis required to reach the optima of the original problem can be reduced by more than 97%.
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nair_98a.pdf
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Published date: 1998
Venue - Dates:
39th AIAA/ASME/ASCE/AHS/ACS Structures, Structural Dynamics and Materials Conference and Exhibition, Long Beach, USA, 1998-04-20 - 1998-04-23
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Local EPrints ID: 21234
URI: http://eprints.soton.ac.uk/id/eprint/21234
PURE UUID: 8fee335d-18a7-4c1e-af69-d88548fa5ab3
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Date deposited: 14 Nov 2006
Last modified: 16 Mar 2024 02:53
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Contributors
Author:
P.B. Nair
Author:
R.P. Shimpi
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