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2016-05-28
Design of Linear Antenna Arrays with Low Side Lobes Level Using Symbiotic Organisms Search
By
Progress In Electromagnetics Research B, Vol. 68, 55-71, 2016
Abstract
In this paper, low side lobe radiation pattern (i.e., pencil-beam pattern) synthesis problem is formulated for symmetric linear antenna arrays. Different array parameters (feed current amplitudes, feed current phase, and array elements positions) are considered as the optimizing variables. The newly proposed evolutionary algorithm, Symbiotic Organisms Search (SOS), is employed to solve such a pattern optimization problem. The design objective is to obtain radiation patterns with very low interference in the entire side lobes region. In this context, SOS is used to minimize the maximum side lobe level (SLL) and impose nulls at specific angles for isotropic linear antenna arrays by optimizing different array parameters (position, amplitude, and phase). The obtained results show the effectiveness of SOS algorithm compared to other well-known optimization methods, like Particle Swarm Optimization (PSO), Biogeography-based optimization (BBO), Genetic Algorithm (GA), Firefly Algorithm (FA), and Taguchi method. Unlike other optimization methods, SOS is free of tuning parameters; one just has to set the value of the population size and the number of iterations. Moreover, SOS is robust and is characterized by relatively fast convergence and ease of implementation.
Citation
Nihad I. Dib, "Design of Linear Antenna Arrays with Low Side Lobes Level Using Symbiotic Organisms Search," Progress In Electromagnetics Research B, Vol. 68, 55-71, 2016.
doi:10.2528/PIERB16032504
References

1. Balanis, C., Antenna Theory: Analysis and Design, 4th Ed., John Wiley & Sons, New York, 2016.

2. Recioui, A., "Optimization of antenna arrays using different strategies based on Taguchi method," Arabian Journal for Science and Engineering (Arab. J. Sci. Eng.), Vol. 39, No. 2, 935-944, February 2014.
doi:10.1007/s13369-013-0644-8

3. Sharaqa, A. and N. Dib, "Design of linear and elliptical antenna arrays using biogeography based optimization," Arabian Journal for Science and Engineering (Arab. J. Sci. Eng.), Vol. 39, No. 4, 2929-2939, April 2014.
doi:10.1007/s13369-013-0794-8

4. Alshdaifat, N. and M. Bataineh, "Optimizing and thinning planar arrays using Chebyshev distribution and improved particle swarm optimization," Jordanian J. of Computers and Information Technology (JJCIT), Vol. 1, No. 1, 31-40, December 2015.

5. Perez Lopez, J. and J. Basterrechea, "Hybrid particle swarm-based algorithms and their application to linear array synthesis," Progress In Electromagnetics Research, Vol. 90, 63-74, 2009.
doi:10.2528/PIER08122212

6. Bataineh, M. and J. Ababneh, "Synthesis of aperiodic linear phased antenna arrays using particle swarm optimization," Electromagnetics, Vol. 26, No. 7, 531-541, 2006.
doi:10.1080/02726340600872948

7. Singh, U., H. Kumar, and T. Kamal, "Linear array synthesis using biogeography based optimization," Progress In Electromagnetics Research M, Vol. 11, 25-36, 2011.

8. Khodier, M. and C. Christodoulou, "Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization," IEEE Trans. on Antennas andPropagation, Vol. 53, No. 8, 2674-2679, August 2005.
doi:10.1109/TAP.2005.851762

9. Basu, B. and G. Mahanti, "Firefly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antennas," Progress In Electromagnetics Research B, Vol. 32, 169-190, 2011.
doi:10.2528/PIERB11053108

10. Oliveri, G. and L. Poli, "Synthesis of monopulse sub-arrayed linear and planar array antennas with optimized sidelobes," Progress In Electromagnetics Research, Vol. 99, 109-129, 2009.
doi:10.2528/PIER09092510

11. Dib, N., S. Goudos, and H. Muhsen, "Application of Taguchi's optimization method and self-adaptive diĀ®erential evolution to the synthesis of linear antenna arrays," Progress In Electromagnetics Research, Vol. 102, 159-180, 2010.
doi:10.2528/PIER09122306

12. Shihab, M., Y. Najjar, N. Dib, and M. Khodier, "Design of non-uniform circular antenna arrays using particle swarm optimization," Journal of Electrical Engineering, Vol. 59, No. 4, 216-220, 2008.

13. Haupt, R. L., "Optimized weighting of uniform subarrays of unequal sizes," IEEE Trans. on Antennas and Propagation, Vol. 55, No. 4, 120-1210, 2007.
doi:10.1109/TAP.2007.893406

14. Cheng, M. and D. Prayogo, "Symbiotic organism search: A new metaheuristic optimization algorithm," Computers and Structures, Vol. 139, 98-112, 2014.
doi:10.1016/j.compstruc.2014.03.007

15. Verma, S., S. Saha, and V. Mukherjee, "A novel symbiotic organisms search algorithm for congestion management in deregulated environment," Journal of Experimental & Theoretical Artificial Intelligence, 2015, DOI: 10.1080/0952813X.2015.1116141.

16. Khodier, M. and M. Al-Aqeel, "Linear and circular array optimization: A study using particle swarm intelligence," Progress In Electromagnetics Research B, Vol. 15, 347-373, 2009.
doi:10.2528/PIERB09033101

17. Sharaqa, A., Biogeography-based optimization and its application in electromagnetics, Master thesis, Jordan Univ. of Science and Technology, Jordan, 2012.

18. Chen, K., Z. He, and C. Han, "A modified real GA for the sparse linear array synthesis with multiple constraints," IEEE Trans. on Antennas and Propagation, Vol. 54, No. 7, 2169-2173, 2006.
doi:10.1109/TAP.2006.877211

19. Zhang, L., Y. Jiao, B. Chen, and F. Zhang, "Synthesis of linear aperiodic arrays using a self adaptive hybrid differential evolution algorithm," IET Microw. Antennas Propag., Vol. 5, No. 12, 1524-1528, 2011.
doi:10.1049/iet-map.2010.0429

20. Kumar, B. and G. Branner, "Design of unequally spaced arrays for performance improvement," IEEE Trans. on Antennas and Propagation, Vol. 47, No. 3, 511-523, 1999.
doi:10.1109/8.768787