SIDELOBE REDUCTION OF LINEAR ANTENNA ARRAY BY POSITION-ONLY CONTROL USING NOVEL METAHEURISTIC OPTIMIZATION METHODS

Yıl 2022, Cilt: 30 Sayı: 2, 171 - 179, 18.08.2022

Ali Durmuş Zafer Yıldırım

https://doi.org/10.31796/ogummf.1012709

Öz

In this study, the positions only of linear antenna array (LAA) elements are optimized for the first time in the literature by using Honey Badger Algorithm (HBA) and Chameleon Swarm Algorithm (CSA) methods, one of the new metaheuristic algorithms, for antenna design with the desired radiation pattern. LAAs with 10, 12, 24 and 32 elements are considered in the simulations. While designing the antenna, the half-beam power width (HPBW) of the antenna array is kept at a minimum level and the maximum sidelobe level (MSL) is suppressed as much as possible. In addition, to test the performance and accuracy of the HBA and CSA methods, the results obtained with the swarm-based metaheuristic algorithms Artificial Bee Colony (ABC) and Particular Swarm Algorithm (PSO) methods, which are quite well known in the literature are compared. As a result of these comparisons, the antenna design with high directivity, gain and desired pattern has been successfully obtained with the HBA and CSA optimization methods.

Anahtar Kelimeler

Artificial intelligence, Optimization, Antenna array synthesis, Maximum sidelobe reduction

YENİ METASEZGİSEL OPTİMİZASYON YÖNTEMLERİNİ KULLANARAK YALNIZCA KONUM KONTROLÜYLE DOĞRUSAL ANTEN DİZİLERİNİN YAN DEMET SEVİYELERİNİN BASTIRILMASI

Öz

Bu çalışmada, istenen ışıma diyagramına sahip anten tasarımı için yeni metasezgisel algoritmalardan biri olan Bal Porsuğu Algoritması (Honey Badger Algorithm-HBA) ve Bukalemun Sürüsü Algoritması (Chameleon Swarm Algorithm-CSA) yöntemleri kullanılarak lineer anten dizi (Linear Antenna Array-LAA) elemanlarının konumları optimize edilmiştir. Simülasyonlarda 10, 12, 24 ve 32 elemanlı Lineer anten dizileri dikkate alınmıştır. Anten tasarımı yapılırken dizinin yarı demet güç genişliği (Half Power Beam Width-HPBW) minimum seviyede tutulmuş ve maksimum yan demet seviyesi mümkün olduğunca bastırılmıştır. Ayrıca HBA ve CSA yöntemlerinin performans ve doğruluğunu test etmek için literatürde oldukça iyi bilinen sürü tabanlı metasezgisel algoritmalar Yapay Arı Kolonisi (ABC) ve Parçacık Sürüsü Optimizasyonu (PSO) yöntemleri ile elde edilen sonuçlar karşılaştırılarak verilmiştir. Bu karşılaştırmalar sonucunda, HBA ve CSA optimizasyon yöntemleri ile yüksek yönlülük, kazanç ve istenilen ışıma diyagramına sahip anten tasarımı başarılı bir şekilde elde edilmiştir.

Anahtar Kelimeler

Yapay zeka, Optimizasyon, Anten dizi sentezi, Maksimum yan demet bastırma

Kaynakça

• Balanis, C. A. (Ed.). (2011). Modern antenna handbook. John Wiley & Sons.doi: https://doi.org/ 10.1002/9780470294154
• Braik, M. S. (2021). Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems. Expert Systems with Applications, 174, 114685. doi: https://doi.org/10.1016/j.eswa.2021.114685
• Banerjee, S. M. I. T. A. & Dwivedi, V. V. (2013). Review of adaptive linear antenna array pattern optimization. International Journal of Electronics and Communication Engineering, 2(1), 25-42.
• Chakravarthy, V. V. S. S. S. Chowdary, P. S. R. Panda, G. Anguera, J. Andújar, A. & Majhi, B. (2018). On the linear antenna array synthesis techniques for sum and difference patterns using flower pollination algorithm. Arabian Journal for Science and Engineering, 43(8), 3965-3977. doi: https://doi.org/10.1007/s13369-017-2750-5
• Durmus, A. Kurban, R. & Karakose, E. (2021). A comparison of swarm-based optimization algorithms in linear antenna array synthesis. Journal of Computational Electronics, 1-12. doi: https://doi.org/10.1007/s10825-021-01711-w
• Ebrahimzade, A. Abedi, H. & Hesari, M. (2016). Gravitational Search Algorithm used to synthesis a planar array antenna for nulling control and side lobe level reduction. In 2016 8th International Symposium on Telecommunications (IST), 391-395. IEEE. doi: https://doi.org/ 10.1109/ISTEL.2016.7881848
• Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110. doi: https://doi.org/ 10.1016/j.matcom.2021.08.013
• Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471. doi: https://doi.org/ 10.1007/s10898-007-9149-x
• Kaur, J. & Goyal, S. (2017). A Comparative study on Linear Array Antenna Pattern Synthesis using Evolutionary Algorithms. International Journal of Advanced Research in Computer Science, 8(5).
• Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks, 4, pp. 1942-1. doi: https://doi.org/ 10.1109/ICNN.1995.488968
• Khodier, M. (2019). Comprehensive study of linear antenna array optimisation using the cuckoo search algorithm. IET Microwaves, Antennas & Propagation, 13(9), 1325-1333. doi: https://doi.org/ 10.1049/iet-map.2018.5649
• Laseetha, T. J. & Sukanesh, R. (2012). Investigations on the synthesis of uniform linear antenna array using biogeography-based optimization techniques. International Journal of Bio-Inspired Computation, 4(2), 119-130.948). IEEE. doi: 10.1504/IJBIC.2012.047186
• Laseetha, T. J. & Sukanesh, R. (2011). Synthesis of linear antenna array using genetic algorithm to maximize side lobe level reduction. International Journal of Computer Applications, 20(7), 27-33. doi: https://doi.org/ 10.1.1.206.4531
• Li, X. Li, W. T. Shi, X. W. Yang, J. & Yu, J. F. (2013). Modified differential evolution algorithm for pattern synthesis of antenna arrays. Progress in Electromagnetics Research, 137, 371-388. doi: https://doi.org/ 10.2528/PIER13011207
• Liang, S. Fang, Z. Sun, G. Liu, Y. Qu, G. & Zhang, Y. (2020). Sidelobe reductions of antenna arrays via an improved chicken swarm optimization approach. IEEE Access, 8, 37664-37683.doi: https://doi.org/ 10.1109/ACCESS.2020.2976127
• Mangoud, M. A. A. & Elragal, H. M. (2009). Antenna array pattern synthesis and wide null control using enhanced particle swarm optimization. Progress In Electromagnetics Research B, 17, 1-14. doi: https://doi.org/ 10.2528/PIERB09070205
• Mirjalili, S. & Dong, J. S. (2020). Multi-objective optimization using artificial intelligence techniques. Springer. doi: https://doi.org/10.1007/978-3-030-24835-2
• Recioui, A. (2012). Side lobe level reduction in linear array pattern synthesis using particle swarm optimization. Journal of Optimization Theory and Applications, 153(2), 497-512. doi: https://doi.org/ 10.1007/s10957-011-9953-9
• Saxena, P. & Kothari, A. (2016). Optimal pattern synthesis of linear antenna array using grey wolf optimization algorithm. International Journal of Antennas and Propagation. .doi: 10.1155/2016/1205970
• Shrivastava, S. & Cecil, K. (2012). Performance analysis of linear antenna array using genetic algorithm. International Journal of Engineering and Innovative Technology (IJEIT), 2(5). doi: https://doi.org/ 10.1.1.1065.9969
• Shrivastava, S. (2013). Broadside linear antenna array synthesis using Genetic Algorithm. International Journal of Scientific Research Engineering & Technology (IJSRET), 2, 332-336. doi: https://doi.org/ 10.1.1.1058.7409
• Singh, U. & Salgotra, R. (2018). Synthesis of linear antenna array using flower pollination algorithm. Neural Computing and Applications, 29(2), 435-445. doi: https://doi.org/10.1007/s00521-016-2457-7
• Singh, U. & Salgotra, R. (2017). Pattern synthesis of linear antenna arrays using enhanced flower pollination algorithm. International Journal of Antennas and Propagation. doi: https://doi.org/ 10.1155/2017/7158752