A new multi-frequency vibrational genetic algorithm in radar cross section minimization problems

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.

Geliştirilmiş titreşimli genetik algoritmanın radar kesit alanı minimizasyonu probleminde uygulaması

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.

PDF

___

[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 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