Abstract
This paper discusses the application of two stochastic optimization methods viz. Simulated Annealing (SA) and Genetic Algorithms (GA) in conceptual design of commuter aircraft. Brief description of a methodology for integrated trajectory and configuration optimization of commuter aircraft is provided, along with the three different objective functions. The features of SIMANN SA algorithm and GOOD GA program are then outlined. The results obtained with these two techniques are compared with those obtained with a proprietary gradient search procedure RQPMIN for all the three objective functions. It was seen that SIMANN & GOOD come up with similar or better configurations compared to RQPMIN. While SIMANN & GOOD required much larger number of evaluations per run compared to RQPMIN, they were found to be less prone to getting trapped in the local minima.
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References
M. F. Bramlette and R. Cusic. A comparitive evaluation of search methods applied to parametric design of aircraft. In Proc. of 3rd International conference on Genetic Algorithms, 1989.
A. Corona, M. Marchesi, C. Martini, and S. Ridella. Minimizing multimodal functions of continuous variables with the Simulated Annealing algorithm. ACM Transactions on Mathematical Software, 13 (3): 262 - 280, September 1987.
Y. Crispin. Aircraft conceptual optimization using Simulated Evolution. In Proc. of 32nd Aerospace Sciences meeting, Reno, NV, USA, number AIAA 94 - 0092, January 1994.
W. L. Goffe, G. D. Ferrier, and J. Rogers. Global optimization of statistical functions with Simulated Annealing. Journal of Econometrics, 60: 65 - 100, 1994.
D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, USA, 1st edition, 1989.
L. R. Jenkinson and D. Simos. The study of energy-efficient short-haul aircraft with emphasis on environmental effects. Technical Report TT87R01, Department of Transport Technology, Loughborough University of Technology, Loughborough, UK, 1987.
C. M. Kalker-Kalkman and M. F. Offermans. A general design program based on Genetic Algorithms with applications. In Proceedings of 21st ASME Design Automation Conference, Boston, USA, September 1995.
S. Kirkpatrick, C. D. Gelatt Jr, and M. P. Vecchi. Optimization by Simulated Annealing. Science, 220: 671 - 680, 1983.
C.-Y. Lin and P. Hajela. Genetic Algorithms in optimization problems with discrete and integer design variables. Engineering Optimization, 19: 309 - 327, 1992.
N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller. Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21: 1087 - 1090, 1953.
R. Pant. Application of stochastic optimization techniques for aircraft conceptual design optimization. In Proceedings of the First World congress on Structural é Multidisciplinary Optimization, Goslar, Germany, pages 827-832, May-June 1995.
D. Simos and L.R. Jenkinson. Optimization of the Conceptual Design and Mission Profiles of Short-Haul aircraft. Journal of Aircraft, 25 (7): 618 - 624, July 1988.
J. J. Skrobanski. RQPMIN version 2.0 user guide (unpublished). Technical report, MVA Consultancy, London, UK, October 1992.
J. Smith and C. Lee. The RAE combat aircraft Multi-Variate Optimization method. In Proceedings of the AIAA/AHS/ASEE Aircraft Design, Systems & Operations conference, Seattle, USA, AIAA 89-2080, July-August 1989.
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© 1997 B. G. Teubner Stuttgart
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Pant, R., Kalker-Kalkman, C.M. (1997). Optimal Trajectory & Configuration of Commuter Aircraft with Stochastic and Gradient based Methods. In: Brøns, M., Bendsøe, M.P., Sørensen, M.P. (eds) Progress in Industrial Mathematics at ECMI 96. European Consortium for Mathematics in Industry, vol 9. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-322-96688-9_39
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DOI: https://doi.org/10.1007/978-3-322-96688-9_39
Publisher Name: Vieweg+Teubner Verlag, Wiesbaden
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Online ISBN: 978-3-322-96688-9
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