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GELİŞTİRİLMİŞ TİTREŞİMLİ GENETİK ALGORİTMANIN RADAR KESİT ALANI MİNİMİZASYONU PROBLEMİNDE UYGULAMASI

A NEW MULTI-FREQUENCY VIBRATIONAL GENETIC ALGORITHM IN RADAR CROSS SECTION MINIMIZATION PROBLEMS

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Abstract (2. Language): 
Within this study, it is aimed to provide an efficient stochastic algorithm for different optimization problems. For this purpose, as a search method, multi frequency vibrational genetic algorithm [m-VGA] is improved and used to accelerate the genetic algorithm for radar cross section minimization problem. From the results obtained, it is concluded that m-VGA decreased the required time for the minimized radar cross section solution beside its simplicity. Low population rate and short generation cycle are the main benefits of the new genetic algorithm.
Abstract (Original Language): 
Günümüzde endüstriyel manada optimizasyon problemlerinin çözümü genellikle gradyan esaslı yöntemlerde aranmaktadır. Bu yönelimdeki en önemli faktör gradyan esaslı yöntemlerin hızlı çözüm vermesidir. Bununla beraber en azından akademik bağlamda sezgisel yöntemlerin kullanımı ise gittikçe yaygınlaşmaktadır. Sezgisel yöntemlerin pek çok avantajı olmasına rağmen en büyük dezavantajı hesaplama sürelerinin çok zaman almasıdır. Bu nedenle en uygun çözüme ulaşmak için sürenin kısaltılması üzerinde en çok çalışılan konulardan biridir. Yapılan bu çalışmada da literatürdeki titreşimli genetik algoritma geliştirilerek daha çabuk sonuç veren versiyonu elde edilmiş ve test alanı olarak radar kesit alanı minimizasyonu sorunsalı ele alınmıştır. Elde edilen sonuçlara göre yeni algoritma başarılı bir performans göstermektedir.
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REFERENCES

References: 

[1] Vanderplaats, G.N., 1984. Numerical
Optimization Techniques for Engineering Design.
McGraw-Hill Book Company, New York.
[2] Back T., Fogel D., and Michalewicz Z.,
Evolutionary Computation I : Basic Algorithms and
Operators, Institute Of Physics Publishing, 2000,
Bristol BS1 6BE, United Kingdom, p. viii/preface.
[3] Holland J. H., “Adaptation in Natural and
Artificial Systems”, the University of Michigan Press,
1975, p. 21-22.
[4] Wang J.F., Periaux J., and Sefrioui M., Parallel
Evolutionary Algorithms for Optimization Problems
in Aerospace Engineering, Journal of Computational
and Applied Mathematics 149 [2002] 155-169.
[5] Jones B. R., Crossley W. A., Lyrintzis A. S.,
“Aerodynamic and Aero acoustic Optimization of
Airfoils via a Parallel Genetic Algorithm”, Purdue
University, 1998.
[6] Vicini A., Quagliarella D., “Airfoil And Wing
Design Through Hybrid Optimization Strategies”,
Presented As Paper 98-2729 At The AIAA 16th
Applied Aerodynamics Conference, Albuquerque,
New Mexico, June 1998.
[7] Muyla F., Dumas L., and Herberta V., Hybrid
method for aerodynamic shape optimization in
automotive industry, Computers & Fluids 33 [2004]
849–858
[8] Poloni C., Genetic algorithms in engineering
and computer science: Hybrid GA for multi objective
aerodynamic shape optimization. England: John Wiley
and Sons, 1995. p. 397–416.
[9] Ong, Y. S., Nair, P. B. and Keane, A. J.,
“Evolutionary Optimization of Computationally
Expensive Problems via Surrogate Modeling,” AIAA
Journal, Vol. 41, No. 4, 2003, pp. 687-696.
[10] Jin, Y., Olhofer, M., and Sendhoff, B., “A
Framework for Evolutionary Optimization with
Approximate Fitness Function,” IEEE Transactions
on Evolutionary Computation, Vol. 6, No. 5, 2002, pp
481-494.
[11] Hacioglu, A., “Augmented Genetic Algorithm
with Neural Network and Implementation to Airfoil
Design,” AIAA 2004-4633, 2004.
[12] Hacioglu, A., “A Novel Usage of Neural
Network in Optimization and Implementation to the
Internal Flow Systems,” Aircraft Engineering and
Aerospace Technology, Vol. 77, No. 5, 2005, pp. 369-
375.
[13] Mitchell M., Bradford A., An Introduction to
Genetic Algorithms, The MIT Press, Cambridge,
Massachusetts •Fifth printing, 1999.
[14] Haupt R., Haupt S. E., Practical Genetic
Algorithms, John Wiley & Sons, Inc., New Jersey,
2004.
[15] De Falco I., Cioppa, A. D., Balio, R. D., and
Tarantino, E., “Breeder Genetic Algorithms for Airfoil
Design Optimization”, IEEE Int. Conf. on
Evolutionary Computing, Nagoya, Japan, 1996.
[16] De Falco I., Cioppa, A. D., Lazzetta, A. and
Tarantino, E., “Mijn Mutation Operator for Airfoil
Design Optimization”, Soft Computing in Engineering
Design and Manufacturing, Springer Verlag, 1998, p.
211-220
[17] Hacioglu A., “Using Genetic Algorithm in
Aerodynamic Design and Optimization”, Ph. D.
Thesis, Technical University of Istanbul, 2003.
[18] Hacioglu, A. and Ozkol, _I., 2002, Vibrational
genetic algorithm as a new concept in aerodynamic
A New Multi-Frequency Vibrational Genetic Algorithm in Optimization Problems
PEHLIVANOGLU
17
design. Aircraft Engineering and Aerospace
Technology, 74[3], 228–236.
[19] Ermis, M., Ulengin, F. and Hacioglu, A., 2002,
Vibrational genetic algorithm [VGA] for solving
continuous covering location problems. Lecture Notes
in Computer Science, 2457, 293–302.
[20] Vatandas E., Hacioglu A., Ozkol I., Vibrational
Genetic Algorithm [VGA] and Dynamic Mesh in the
Optimization of 3D Wing Geometries, Inverse
Problems in Science and Engineering, Vol.15, No. 6,
September 2007, 643-657.
[21] Pehlivanoglu Y. V., Baysal O., and Hacioglu
A., Path planning for autonomous UAV via VGA,
Aircraft Engineering and Aerospace Technology: An
International Journal, Volume 79 · Number 4 · 2007 ·
352–359
[22] Brown A., “Fundamentals of Low Radar
Cross-Sectional Aircraft Design,” Journal of Aircraft,
Vol. 30, No. 3, May-June 1993, pp. 289-290
[23] Raymer D., Aircraft Design: A Conceptual
Approach, 3rd Ed., AIAA, Washington, 1999, p. 201-
203
[24] Bondeson A., Yang Y., and Weinerfelt P.,
Optimization Of Radar Cross Section By A Gradient
Method, IEEE Transactions On Magnetics, Vol. 40,
No. 2, March 2004
[25] D. Nair and J. P. Webb, “Optimization of
microwave devices using 3-D finite elements and the
design sensitivity of the frequency response,” IEEE
Trans. Magn., vol. 39, pp. 1325–1328, May 2003.
[26] Ozturk A. K., Implementation Of Physical
Theory Of Diffraction For Radar Cross Section
Calculations, Master Thesis, Bilkent University,
Turkey, July 2002
[27] Chatzigeorgiadis F., Development of Code for
Physical Optics Radar Cross Section Prediction and
Analysis Application, Master's Thesis, Naval Post
Graduate School, California, USA, 2004.
[28] Mosallaei H. and Rahmat-Samii Y., RCS
reduction of canonical targets using genetic algorithm
synthesized RAM, IEEE Trans. Antennas Propagat.,
vol. 48, no. 10, pp. 1594-1606, Oct. 2000.
[29] Makinen R., Periaux J., and Toivanen J.,
Multidisciplinary shape optimization in aerodynamics
and electromagnetics using genetic algorithms", Int. J.
Numer. Methods Fluids, [1998]. Volume 30, Issue 2,
Pages 149 – 159

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