A stand-alone genetic algorithm (GA) and an surrogate-based optimization (SBO) combined with a GA were compared for accuracy and performance. Comparisons took place using the Ackley Function and Rastrigin's Function, two functions with multiple local maxima and minima that could cause problems for more traditional optimization methods, such as a gradient-based method. The GA and SBO with GA were applied to the functions through a fortran interface and it was found that the SBO could use the same number of function evaluations as the GA and achieve at least 5 orders of magnitude greater accuracy through the use of surrogate evaluations.
The two optimization methods were used in conjunction with computational fluid dy- namics (CFD) analysis to optimize the shape of a bumpy airfoil section. Results of opti- mization showed that the use of an SBO can save up to 553 hours of CPU time on 196 cores when compared to the GA through the use of surrogate evaluations. Results also show the SBO can achieve greater accuracy than the GA in a shorter amount of time, and the SBO can reduce the negative effects of noise in the simulation data while the GA cannot.
| Identifer | oai:union.ndltd.org:UTAHS/oai:digitalcommons.usu.edu:etd-1861 |
| Date | 01 December 2011 |
| Creators | Johansen, Todd A. |
| Publisher | DigitalCommons@USU |
| Source Sets | Utah State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | All Graduate Theses and Dissertations |
| Rights | Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact Andrew Wesolek (andrew.wesolek@usu.edu). |
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