Vol. 68
Latest Volume
All Volumes
PIERB 105 [2024] PIERB 104 [2024] PIERB 103 [2023] PIERB 102 [2023] PIERB 101 [2023] PIERB 100 [2023] PIERB 99 [2023] PIERB 98 [2023] PIERB 97 [2022] PIERB 96 [2022] PIERB 95 [2022] PIERB 94 [2021] PIERB 93 [2021] PIERB 92 [2021] PIERB 91 [2021] PIERB 90 [2021] PIERB 89 [2020] PIERB 88 [2020] PIERB 87 [2020] PIERB 86 [2020] PIERB 85 [2019] PIERB 84 [2019] PIERB 83 [2019] PIERB 82 [2018] PIERB 81 [2018] PIERB 80 [2018] PIERB 79 [2017] PIERB 78 [2017] PIERB 77 [2017] PIERB 76 [2017] PIERB 75 [2017] PIERB 74 [2017] PIERB 73 [2017] PIERB 72 [2017] PIERB 71 [2016] PIERB 70 [2016] PIERB 69 [2016] PIERB 68 [2016] PIERB 67 [2016] PIERB 66 [2016] PIERB 65 [2016] PIERB 64 [2015] PIERB 63 [2015] PIERB 62 [2015] PIERB 61 [2014] PIERB 60 [2014] PIERB 59 [2014] PIERB 58 [2014] PIERB 57 [2014] PIERB 56 [2013] PIERB 55 [2013] PIERB 54 [2013] PIERB 53 [2013] PIERB 52 [2013] PIERB 51 [2013] PIERB 50 [2013] PIERB 49 [2013] PIERB 48 [2013] PIERB 47 [2013] PIERB 46 [2013] PIERB 45 [2012] PIERB 44 [2012] PIERB 43 [2012] PIERB 42 [2012] PIERB 41 [2012] PIERB 40 [2012] PIERB 39 [2012] PIERB 38 [2012] PIERB 37 [2012] PIERB 36 [2012] PIERB 35 [2011] PIERB 34 [2011] PIERB 33 [2011] PIERB 32 [2011] PIERB 31 [2011] PIERB 30 [2011] PIERB 29 [2011] PIERB 28 [2011] PIERB 27 [2011] PIERB 26 [2010] PIERB 25 [2010] PIERB 24 [2010] PIERB 23 [2010] PIERB 22 [2010] PIERB 21 [2010] PIERB 20 [2010] PIERB 19 [2010] PIERB 18 [2009] PIERB 17 [2009] PIERB 16 [2009] PIERB 15 [2009] PIERB 14 [2009] PIERB 13 [2009] PIERB 12 [2009] PIERB 11 [2009] PIERB 10 [2008] PIERB 9 [2008] PIERB 8 [2008] PIERB 7 [2008] PIERB 6 [2008] PIERB 5 [2008] PIERB 4 [2008] PIERB 3 [2008] PIERB 2 [2008] PIERB 1 [2008]
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 di®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