Abstract

Biogeography is the study of distribution of biological species, over space and time, among random habitats. Recently introduced Biogeography Based Optimization (BBO) is a technique, where solutions of the problem are termed as habitats. Feature, i. e. , Suitability Index Variable (SIV), sharing among various habitats is made to occur with migration operator where as exploration of new SIVs is done with mutation operator. Yagi-Uda antenna is a widely used antenna design due to various useful properties of high gain, low cost and ease of construction. Immigration Refusal BBO (IRBBO) is a BBO variant introduced with the objective of improved performance and faster convergence. Designing a Yagi-Uda antenna involves determination of element lengths and spacings between them to get desired radiation characteristics. The gain of Yagi-Uda antenna is difficult to optimize as there is no analytical formula to determine gain directly, it makes relationship between antenna parameters and its characteristics highly complex and non-linear. In this paper, fifteen-element Yagi-Uda antenna is optimized for maximum gain using IRBBO. The obtained results are compared with Standard BBO, Bi-Swarm optimization, Ellipsoid Algorithm and Genetic Algorithm (GA). IRBBO shows better results among other compared optimization techniques for Yagi-Uda antenna design problem

References

• E. E. Altshuler and D. S. Linden. Wire-antenna Designs using Genetic Algorithms. Antennas and Propagation Magazine, IEEE, 39(2):33–43, 1997.
• A. N. Amaral, U. C. Resende, and E. N. Gonc¸alves. Yagi-uda antenna optimization by elipsoid algorithm. pages 503– 506, 2011.
• A. Wallace. The Geographical Distribution of Animals. Boston, MA: Adamant Media Corporation, Two:232–237, 2005.
• 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.
• JH Bojsen, H. Schjaer-Jacobsen, E. Nilsson, and J. Bach Andersen. Maximum Gain of Yagi–Uda Arrays. Electronics Letters, 7(18):531–532, 1971.
• 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.
• C. Chen and D. Cheng. Optimum Element Lengths for Yagi-Uda Arrays. IEEE Transactions on Antennas and Propagation,, 23(1):8–15, 1975.
• D. Cheng and C. Chen. Optimum Element Spacings for Yagi-Uda Arrays. IEEE Transactions on Antennas and Propagation,, 21(5):615–623, 1973.
• D. K. Cheng. Optimization Techniques for Antenna Arrays. Proceedings of the IEEE, 59(12):1664–1674, 1971.
• D. K. Cheng. Gain Optimization for Yagi-Uda Arrays. Antennas and Propagation Magazine, IEEE, 33(3):42–46, 1991.
• 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.
• C. Darwin. The Orign of Species. New York : gramercy, Two:398–403, 1995.
• D. Du, D. Simon, and M. Ergezer. Biogeography-based Optimization Combined with Evolutionary Strategy and Immigration Refusal. IEEE, 1:997–1002, 2009.
• 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.
• 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.
• E. A. Jones andW. T. Joines. Design of Yagi-Uda Antennas using Genetic Algorithms. IEEE Transactions on Antennas and Propagation,, 45(9):1386–1392, 1997.
• Y. Kuwahara. Multiobjective Optimization Design of Yagi- Uda Antenna. IEEE Transactions on Antennas and Propagation,, 53(6):1984–1992, 2005.
• J. Y. Li. Optimizing Design of Antenna using Differential Evolution. IEEE, 1:1–4, 2007.
• J. Y. Li. A bi-swarm optimizing strategy and its application of antenna design. Journal of Electromagnetic Waves and Applications, 23(14-15):1877–1886, 2009.
• Y. Li, F. Yang, J. OuYang, and H. Zhou. Yagi-uda antenna optimization based on invasive weed optimization method. Electromagnetics, 31(8):571–577, 2011.
• R. H. MacArthur and E. O. Wilson. The Theory of Island Biogeography. Princeton Univ Pr, 1967.
• 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.
• 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.
• G. Sachdeva, D. Singh, and S. Singh. Gain Optimization of 15-Element Yagi-Uda Antenna using BBO. In proceeding of IEEE, 2013.
• 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.
• D. Simon. Biogeography-based Optimization. IEEE Transactions on Evolutionary Computation,, 12(6):702–713, 2008.
• 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.
• 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.
• Shintaro Uda and Yasuto Mushiake. Yagi-Uda Antenna. Maruzen Company, Ltd, 1954.
• N. V. Venkatarayalu and T. Ray. Single and Multi-Objective Design of Yagi-Uda Antennas using Computational Intelligence. IEEE, 2:1237–1242, 2003.
• 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.
• 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.
• H. Yagi. Beam Transmission of Ultra Short Waves. Proceedings of the Institute of Radio Engineers, 16(6):715– 740, 1928. 5