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Reseach Article

Multi-objective Gain-Impedance Optimization of Yagi-Uda Antenna using NSBBO and NSPSO

by Satvir Singh, Etika Mittal, Gagan Sachdeva
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 15
Year of Publication: 2012
Authors: Satvir Singh, Etika Mittal, Gagan Sachdeva
10.5120/8964-3193

Satvir Singh, Etika Mittal, Gagan Sachdeva . Multi-objective Gain-Impedance Optimization of Yagi-Uda Antenna using NSBBO and NSPSO. International Journal of Computer Applications. 56, 15 ( October 2012), 1-6. DOI=10.5120/8964-3193

@article{ 10.5120/8964-3193,
author = { Satvir Singh, Etika Mittal, Gagan Sachdeva },
title = { Multi-objective Gain-Impedance Optimization of Yagi-Uda Antenna using NSBBO and NSPSO },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 15 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number15/8964-3193/ },
doi = { 10.5120/8964-3193 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:14.767818+05:30
%A Satvir Singh
%A Etika Mittal
%A Gagan Sachdeva
%T Multi-objective Gain-Impedance Optimization of Yagi-Uda Antenna using NSBBO and NSPSO
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 15
%P 1-6
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biogeography-Based Optimization (BBO) is a population based algorithm which has shown impressive performance over other Evolutionary Algorithms (EAs). BBO algorithm is based on the study of distribution of biological organisms over space and time. Yagi- Uda antenna design is most widely used antenna at VHF and UHF frequencies due to high gain, directivity and ease of construction. However, designing a Yagi-Uda antenna, that involves determination of optimal wire-lengths and their spacings, is highly complex and non-linear engineering problem. It further complicates as multiple objectives, viz. gain, and impedance, etc. , are required to be optimized due to their conflicting nature, i. e. , reactive antenna impedance increases significantly as antenna gain is intended to increase. In this paper Non-dominated Sorting BBO (NSBBO) is proposed and where standard and blended variants of BBO are investigated in optimizing six-element Yagi-Uda antenna designs for multiple objectives, viz. , gain and impedance, where ranking of potential solutions is done using non-dominated sorting. The simulation results of BBO variants and Particle Swarm Optimization (PSO) are presented in the ending sections of the paper that depict clearly that NSBBO with blended migration operator is best option among all.

References
  1. E. E. Altshuler and D. S. Linden. Wire-antenna Designs using Genetic Algorithms. Antennas and Propagation Magazine, IEEE, 39(2):33–43, 1997.
  2. A. Wallace. The Geographical Distribution of Animals. Boston, MA: Adamant Media Corporation, Two:232–237, 2005.
  3. S. Baskar, A. Alphones, P N Suganthan, and J J Liang. Design of Yagi-Uda Antennas using Comprehensive Learning Particle Swarm Optimisation. IEEE, 152(5):340–346, 2005.
  4. JH Bojsen, H. Schjaer-Jacobsen, E. Nilsson, and J. Bach Andersen. Maximum Gain of Yagi–Uda Arrays. Electronics Letters, 7(18):531–532, 1971.
  5. G. J. Burke and A. J. Poggio. Numerical Electromagnetics Code (NEC) method of moments. NOSC Tech. DocLawrence Livermore National Laboratory, Livermore, Calif, USA, 116:1–131, 1981.
  6. C. Chen and D. Cheng. Optimum Element Lengths for Yagi-Uda Arrays. IEEE Transactions on Antennas and Propagation,, 23(1):8–15, 1975.
  7. D. Cheng and C. Chen. Optimum Element Spacings for Yagi-Uda Arrays. IEEE Transactions on Antennas and Propagation,, 21(5):615–623, 1973.
  8. D. K. Cheng. Optimization Techniques for Antenna Arrays. Proceedings of the IEEE, 59(12):1664–1674, 1971.
  9. D. K. Cheng. Gain Optimization for Yagi-Uda Arrays. Antennas and Propagation Magazine, IEEE, 33(3):42–46, 1991.
  10. D. Correia, A. J. M. Soares, and M. A. B. Terada. Optimization of gain, impedance and bandwidth in Yagi-Uda Antennas using Genetic Algorithm. IEEE, 1:41–44, 1999.
  11. C. Darwin. The Orign of Species. New York : gramercy, Two:398–403, 1995.
  12. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. Evolutionary Computation, IEEE Transactions on, 6(2):182– 197, 2002.
  13. R. C. Eberhart, Y. Shi, and J. Kennedy. Swarm Intelligence. Morgan Kaufmann Publisher, 2001.
  14. H. Ehrenspeck and H. Poehler. A New Method for Obtaining Maximum Gain from Yagi Antennas. IRE Transactions on Antennas and Propagation,, 7(4):379–386, 1959.
  15. R. M. Fishenden and E. R. Wiblin. Design of Yagi Aerials. Proceedings of the IEE-Part III: Radio and Communication Engineering, 96(39):5, 1949.
  16. E. A. Jones and W. T. Joines. Design of Yagi-Uda Antennas using Genetic Algorithms. IEEE Transactions on Antennas and Propagation,, 45(9):1386–1392, 1997.
  17. J. Kennedy and R. Eberhart. Particle swarm optimization. 4:1942–1948, 1995.
  18. Y. Kuwahara. Multiobjective Optimization Design of Yagi- Uda Antenna. IEEE Transactions on Antennas and Propagation,, 53(6):1984–1992, 2005.
  19. J. Y. Li. Optimizing Design of Antenna using Differential Evolution. IEEE, 1:1–4, 2007.
  20. R. H. MacArthur and E. O. Wilson. The Theory of Island Biogeography. Princeton Univ Pr, 1967.
  21. T. McTavish and D. Restrepo. Evolving Solutions: The Genetic Algorithm and Evolution Strategies for Finding Optimal Parameters. Applications of Computational Intelligence in Biology, 1:55–78, 2008.
  22. K. E. Parsopoulos and M. N. Vrahatis. Recent Approaches to Global Optimization Problems through Particle Swarm Optimization. Natural computing, 1(2):235–306, 2002.
  23. M. Rattan, M. S. Patterh, and B. S. Sohi. Optimization of Yagi-Uda Antenna using Simulated Annealing. Journal of Electromagnetic Waves and Applications, 22, 2(3):291– 299, 2008.
  24. D. G. Reid. The Gain of an Idealized Yagi Array. Journal of the Institution of Electrical Engineers-Part IIIA: Radiolocation,, 93(3):564–566, 1946.
  25. L. C. Shen. Directivity and Bandwidth of Single-band and Double-band Yagi Arrays. IEEE Transactions on Antennas and Propagation,, 20(6):778–780, 1972.
  26. Y. Shi and R. C. Eberhart. Empirical Study of Particle Swarm Optimization. 3, 1999.
  27. Y. Shi et al. Particle Swarm Optimization: Developments, Applications and Resources. 1:81–86, 2001.
  28. D. Simon. Biogeography-based Optimization. IEEE Transactions on Evolutionary Computation,, 12(6):702–713, 2008.
  29. Satvir Singh and Gagan Sachdeva. Mutation Effects on BBO Evolution in Optimizing Yagi-Uda Antenna Design. In International Conference on Emerging Applications of Information Technology (EAIT2012), 2012.
  30. U. Singh, H. Kumar, and T. S. Kamal. Design of Yagi-Uda Antenna Using Biogeography Based Optimization. IEEE Transactions on Antennas and Propagation,, 58(10):3375– 3379, 2010.
  31. U. Singh, M. Rattan, N. Singh, and M. S. Patterh. Design of a Yagi-Uda Antenna by Simulated Annealing for Gain, Impedance and FBR. IEEE, 1:974–979, 2007.
  32. Shintaro Uda and Yasuto Mushiake. Yagi-Uda Antenna. Maruzen Company, Ltd, 1954.
  33. N. V. Venkatarayalu and T. Ray. Single and Multi-Objective Design of Yagi-Uda Antennas using Computational Intelligence. IEEE, 2:1237–1242, 2003.
  34. N. V. Venkatarayalu and T. Ray. Optimum Design of Yagi- Uda Antennas Using Computational Intelligence. IEEE Transactions on Antennas and Propagation,, 52(7):1811– 1818, 2004.
  35. H. J. Wang, K. F. Man, C. H. Chan, and K. M. Luk. Optimization of Yagi array by Hierarchical Genetic Algorithms. IEEE, 1:91–94, 2003.
  36. H. Yagi. Beam Transmission of Ultra Short Waves. Proceedings of the Institute of Radio Engineers, 16(6):715– 740, 1928.
Index Terms

Computer Science
Information Sciences

Keywords

Non-dominated Sorting Bio-geography Based Optimization (BBO) Particle Swarm Optimization (PSO) Yagi-Uda Antenna Multi-Objective Optimization Antenna Gain Antenna Impedance