This paper proposes a new design optimization framework by integrating evolutionary search and cumulative function approximation. While evolutionary algorithms are robust even under multi-peaks, rugged natures, etc., their computational cost is inferior to ordinary schemes such as gradient-based methods. While response surface techniques such as quadratic approximation can save computational cost for complicated design problems, the fidelity of solution is affected by density of samples. The new framework simultaneously performs evolutionary search and constructs response surfaces. That is, in its early phase the search is performed over roughly but globally approximated surfaces with the relatively small number of samples, and in its later phase the search is performed intensively around promising regions, which are revealed in the preceded phases, over response surfaces enhanced with additional samples. This framework is expected to be able to robustly find the optimal solution with less sampling. An optimization algorithm is implemented by combining a real-coded genetic algorithm and a Voronoi diagram based cumulative approximation, and it is applied to some numerical examples for discussing its potential and promises.
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ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
September 24–28, 2005
Long Beach, California, USA
Conference Sponsors:
- Design Engineering Division and Computers and Information in Engineering Division
ISBN:
0-7918-4739-X
PROCEEDINGS PAPER
Design Optimization by Integration of Evolutionary Search and Cumulative Approximation
Tomoki Ushiro,
Tomoki Ushiro
Osaka University, Osaka, Japan
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Noriyasu Hirokawa
Noriyasu Hirokawa
Kinki University, Wakayama, Japan
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Kikuo Fujita
Osaka University, Osaka, Japan
Tomoki Ushiro
Osaka University, Osaka, Japan
Noriyasu Hirokawa
Kinki University, Wakayama, Japan
Paper No:
DETC2005-85245, pp. 305-319; 15 pages
Published Online:
June 11, 2008
Citation
Fujita, K, Ushiro, T, & Hirokawa, N. "Design Optimization by Integration of Evolutionary Search and Cumulative Approximation." Proceedings of the ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2: 31st Design Automation Conference, Parts A and B. Long Beach, California, USA. September 24–28, 2005. pp. 305-319. ASME. https://doi.org/10.1115/DETC2005-85245
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