volume | PIER Journals

Abstract

The suppression of the side-lobe level (SLL) of antenna arrays is a significant factor that can enhance the reliability and validity of a communication system. Recently, metaheuristic algorithms have been widely implemented in the design of antenna arrays, in order to find the optimal minimization for the side-lobe level of the array's radiation pattern. In this paper, we propose a new hybrid algorithm that combines the characteristics of two stochastic algorithms, Antlion Optimization (ALO) algorithm and Grasshopper Optimization Algorithm (GOA). ALO, which is an evolutionary algorithm, is robust in exploitation and has been effectively used in many articles in the literature. GOA has strong capability of exploration all over the search space due to the swarm nature of the algorithm, which has been proven in several articles in the literature. Therefore, combining these characteristics and overcoming the drawbacks of ALO and GOA are the main motivation behind hybridizing ALO and GOA in one hybrid algorithm. Simulation results show that the proposed hybrid algorithm has a good performance in the radiation pattern optimization of circular antenna array (CAA) and fast convergence rate compared with other strong optimization algorithms, which prove the efficiency, robustness, and stability of the hybrid algorithm.

1. Mirjalili, S., "Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm," Knowledge-based Systems, Vol. 89, 228-249, 2015.
doi:10.1016/j.knosys.2015.07.006

2. Spall, J. C., Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, John Wiley & Sons, 2003.
doi:10.1002/0471722138

3. Eberhart, R. C. and J. Kennedy, "A new optimizer using particle swarm theory," Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995.

4. 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, 216-220, 2008.

5. Holland, J. H., "Genetic algorithms," Scientific American, Vol. 267, 66-72, 1992.
doi:10.1038/scientificamerican0792-66

6. Panduro, M., A. Mendez, R. Dominguez, and G. Romero, "Design of non-uniform circular antenna arrays for side lobe reduction using the method of genetic algorithms," International Journal of Electronics and Communications, Vol. 60, 713-717, 2006.
doi:10.1016/j.aeue.2006.03.006

7. Mirjalili, S., S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in Engineering Software, Vol. 69, 46-61, 2014.
doi:10.1016/j.advengsoft.2013.12.007

8. Mirjalili, S., "The ant lion optimizer," Advances in Engineering Software, Vol. 83, 80-98, 2015.
doi:10.1016/j.advengsoft.2015.01.010

9. Dib, N., A. Amaireh, and A. Al-Zoubi, "On the optimal synthesis of elliptical antenna arrays," International Journal of Electronics, Vol. 106, No. 2, 121-133, 2019.
doi:10.1080/00207217.2018.1512658

10. Shah-Hosseini, H., "Principal components analysis by the galaxy-based search algorithm: A novel metaheuristic for continuous optimization," International Journal of Computational Science and Engineering, Vol. 6, 132-140, 2011.
doi:10.1504/IJCSE.2011.041221

11. Colorni, A., M. Dorigo, and V. Maniezzo, "Distributed optimization by ant colonies," Proceedings of the First European Conference on Artificial Life, 1991.

12. Amaireh, A., A. Alzoubi, and N. Dib, "Design of linear antenna arrays using antlion and grasshopper optimization algorithms," IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, 2017.

13. Saremi, S., S. Mirjalili, and A. Lewis, "Grasshopper optimisation algorithm: Theory and application," Advances in Engineering Software, Vol. 105, 30-47, 2017.
doi:10.1016/j.advengsoft.2017.01.004

14. Kaveh, A., T. Bakhshpoori, and E. Afshari, "An efficient hybrid particle swarm and swallow swarm optimization algorithm," Computers and Structures, Vol. 143, 40-59, 2014.
doi:10.1016/j.compstruc.2014.07.012

15. Wolpert, D. and W. Macready, "No free lunch theorems for optimization," IEEE Transactions on Evolutionary Computation, Vol. 1, 67-82, 1997.
doi:10.1109/4235.585893

16. Blum, C., A. Roli, and M. Sampels, Hybrid Metaheuristics — An Emerging Approach to Optimization, Springer, 2008.

17. Mirjalili, S., G. Wang, and L. S. Coelho, "Binary optimization using hybrid particle swarm optimization and gravitational search algorithm," Neural Computing & Applications, Vol. 25, 1423-1435, 2014.
doi:10.1007/s00521-014-1629-6

18. Mafarja, M., I. Aljarah, A. Heidari, A. Hammouri, H. Faris, A. Al-Zoubi, and S. Mirjalili, "Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems," Knowledge-based Systems, Vol. 145, No. 1, 25-45, 2018.
doi:10.1016/j.knosys.2017.12.037

19. Wu, J., H. Wang, N. Li, P. Yao, Y. Huang, Z. Su, and Y. Yu, "Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by adaptive grasshopper optimization algorithm," Aerospace Science and Technology, Vol. 70, 497-510, 2017.

20. Heidari, A., H. Faris, I. Aljarah, and S. Mirjalili, "An efficient hybrid multilayer perceptron neural network with grasshopper optimization," Soft Computing, 1-18, 2018.

21. El-Ghazali, T., Metaheuristics: From Design to Implementation, Wiley & Sons, 2009.

22. Panduro, M. A., C. A. Brizuela, L. I. Balderas, and D. A. Acosta, "A comparison of genetic algorithms, particle swarm optimization and the differential evolution method for the design of scannable circular antenna arrays," Progress In Electromagnetics Research B, Vol. 13, 171-186, 2009.

23. Mahmoud, K., M. Eladawy, R. Bansal, S. Zainud-Deen, and S. Ibrahem, "Analysis of uniform circular arrays for adaptive beamforming applications using particle swarm optimization algorithm," International Journal of RF and Microwave Computer-Aided Engineering, Vol. 18, 42-52, 2008.

24. Guneya, K. and S. Basbug, "A parallel implementation of seeker optimization algorithm for designing circular and concentric circular antenna arrays," Applied Soft Computing, Vol. 22, 287-296, 2014.

25. Balanis, C., Antenna Theory: Analysis and Design, Wiley, 2012.

26. Panduro, M. A. and C. A. Brizuela, "A comparative analysis of the performance of GA, PSO and DE for circular antenna arrays," IEEE Antennas and Propagation Society International Symposium, Charleston, USA, 2009.

27. Reyna, A., M. Panduro, D. Covarrubias, and A. Mendez, "Design of steerable concentric rings array for low side lobe level," Scientia Iranica, Vol. 19, No. 3, 727-732, 2012.

28. Babayigit, B., "Synthesis of concentric circular antenna arrays using dragonfly algorithm," International Journal of Electronics, Vol. 105, 784-793, 2017.

29. Panduro, M. and C. Brizuela, "Evolutionary multi-objective design of non-uniform circular phased arrays," COMPEL International Journal of Computations and Mathematics in Electrical, Vol. 27, No. 2, 551-566, 2008.

30. Panduro, M., C. Brizuela, J. Garza, S. Hinojosa, and A. Maldonado, "A comparison of NSGAII, DEMO, and EM-MOPSO for the multi-objective design of concentric rings antenna arrays," Journal of Electromagnetic Waves and Applications, Vol. 27, No. 9, 1100-1113, 2013.

31. Elizarraras, O., A. Mendez, A. Maldonado, M. Panduro, and D. Covarrubias, "Design of circular array of circular subarrays for scannable pattern using rotational symmetry," IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, 2017.

32. Garza, L., M. Panduro, D. Covarrubias, and A. Maldonado, "Multiobjective synthesis of steerable UWB circular antenna array considering energy patterns," International Journal of Antennas and Propagation, Vol. 2015, Article ID 789094, 9 pages, 2015.

33. Sharaqa, A. and N. Dib, "Design of linear and circular antenna arrays using biogeography based optimization," IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, 2011.

Dr. Anas Atef Amaireh

Dr. Asem Sh. Al-Zoubi

Prof. Nihad I. Dib