Multi-verse optimization algorithm for optimal synthesis of phase-only reconfigurable linear array of mutually coupled parallel half-wavelength dipole antennas placed at finite distances from the ground plane

Document Type : Article

Authors

1 Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur-713209, India

2 Department of Electrical and Communication Engineering, College of Engineering, NUST, Sultanate of Oman

Abstract

Researchers on antenna arrays usually neglect the effect of mutual coupling of antennas placed in proximity to each other. The interchange of electromagnetic energy happening between an antenna and a far field point depends not only on the transmitting antenna, but also from its neighbouring antennas. This effect is referred to as mutual coupling between dipole antenna elements and it is considered here in the synthesis of phase only reconfigurable antenna arrays. The main objective of this work is to produce desired Side Lobe Level and Voltage Standing Wave Ratio in addition to few other radiation pattern parameters. Multiverse Optimization algorithm is employed for the purpose of generating voltage amplitude and discrete phase distributions in the dipole elements for the generation of flat-top beam/pencil beam patterns. These two patterns share the common amplitude distributions and differ in phase distributions. Results obtained using MATLAB simulations prove that this algorithm accomplished its task successfully and is also found to be superior over other algorithms like Particle Swarm Optimization, Grey Wolf Optimization and Imperialist Competitive Optimization algorithms.

Keywords


References:
1. Balanis, C.A., Antenna Theory: Analysis and Design, 2nd Ed., Singapore: John Wiley and Sons (Asia) (2003).
2. Elliott, R.S., Antenna Theory and Design, New York, USA: Prentice-Hall (1981).
3. Bucci, O.M., Mazzarella, G., and Panariello, G. "Reconfigurable arrays by phase-only control", IEEE Transactions on Antennas and Propagation, 39(7), pp. 919-925 (1991).
4. Durr, M., Trastoy, A., and Ares, F. "Multiple-pattern linear antenna arrays with single prefixed amplitude distributions: modified Woodward-Lawson synthesis", Electronics Letters, 36(16), pp. 1345-1346 (2000).
5. Baskar, S., Alphones, A., and Suganthan, P.N. "Genetic algorithm based design of a reconfigurable antenna array with discrete phase shifter", Microwave and Optical Technology Letters, 45, pp. 461-465 (2005).
6. Mahanti, G.K., Chakraborty, A., and Das, S. "Design of phase-differentiated reconfigurable array antennas with minimum dynamic range ratio", IEEE Antennas and Wireless Propagation Letters, 5(1), pp. 262-264 (2006).
7. Mahanti, G.K., Das, S., Chakrabarty, A., et al. "Design of reconfigurable array antennas with minimum variation of active impedances", IEEE Antennas and Wireless Propagation Letters, 5(1), pp. 541-544 (2006).
8. Gies, D. and Rahmat-Samii, Y. "Particle swarm optimization for reconfigurable phase- differentiated array design", Microwave and Optical Technology Letters, 38, pp. 168-175 (2003).
9. Jamunaa, D., Mahanti, G.K., and Hasoon, F.N. "Synthesis of phase-only position optimized reconfigurable uniformly excited linear antenna arrays with a single null placement", Journal of King Saud University - Engineering Sciences, 32(6), pp. 360-367 (2020).
10. Muralidharan, R., Vallavaraj, A., Mahanti, G.K., et al. "QPSO versus BSA for failure correction of linear array of mutually coupled parallel dipole antennas with fixed side lobe level and VSWR", Advances in Electrical Engineering, 2014, Article ID 858290, pp. 1-7 (2014).
11. Singh, H., Sneha, H.L., and Jha, R.M. "Mutual coupling in phased arrays", International Journal of Antennas and Propagation, 2013, Article ID 348123, pp. 1-23 (2013).
12. Lee, K.M. and Chu, R.S. "Analysis of mutual coupling between a finite phased array of dipoles and its feed network", IEEE Transactions on Antennas and Propagation, 36(12), pp. 1681-1699 (1988).
13. Muralidharan, R., Vallavaraj, A., and Mahanti, G.K. "Fire y algorithm for failure correction of linear array of dipole antennas in presence of ground plane with mutual coupling effects", ACES Journal, 30(10), pp. 1122-1128 (2015).
14. Mirjalili, S., Mirjalili, S.M., and Hatamlou, A. "Multiverse optimizer: a nature-inspired algorithm for global optimization", Neural Computing and Applications, 27(2), pp. 495-513 (2016).
15. Faris, H., Aljarah, I., and Mirjalili, S. "Training feedforward neural networks using multi-verse optimizer for binary classification problems", Applied Intelligence, 45(2), pp. 322-332 (2016).
16. Hu, C., Li, Z., Zhou, T., Zhu, A., and Xu, C. "A multi-verse optimizer with levy  flights for numerical optimization and its application in test scheduling for network-on-chip", PLOS ONE, 11(12), e0167341 (2016).
17. Sayed, G.I., Darwish, A., and Hassanien, A.E. "Quantum multi-verse optimization algorithm for optimization problems", Neural Computing & Applications, 31, pp. 2763-2780 (2017).
18. Sulaiman, M., Ahmad, S., Iqbal, J., et al. "Optimal operation of the hybrid electricity generation system using multiverse optimization algorithm", Computational Intelligence and Neuroscience, 2019, Article ID 6192980, 12 pages (2019).
19. Karthikeyan, K. and Dhal, P.K. "Multi verse optimization (MVO) technique based voltage stability analysis through continuation power flow in IEEE 57 bus", Energy Procedia, 117, pp. 583-591 (2017).
20. Ahmed, F. and Hegazy, R. "Multi-verse optimizer for identifying the optimal parameters of PEMFC model", Energy, Elsevier, 143(C), pp. 634-644 (2018).
21. Jia, H., Peng, X., Song, W., et al. "Multiverse optimization algorithm based on levy flight improvement for multithreshold color image segmentation", IEEE Access, 7, pp. 32805-32844 (2019).
22. Hans, R., and Kaur, H. "Binary Multi-Verse Optimization (BMVO) approaches for feature selection", International Journal of Interactive Multimedia and Artificial Intelligence, 6(1), pp. 91-106 (2020).
23. Kennedy, J. and Eberhart, R. "Particle swarm optimization", Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942- 1948 (1995).
24. Bansal, J.C., Singh, P.K., Saraswat, M., et al. "Inertia weight strategies in particle swarm optimization", Third World Congress on Nature and Biologically Inspired Computing, Salamanca, 2011, pp. 633-640 (2011).
25. Atashpaz-Gargari, E. and Lucas, C. "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition", 2007 IEEE Congress on Evolutionary Computation, Singapore, pp. 4661-4667 (2007).
26. Abdollahi, M., Isazadeh, A., and Abdollahi, D. "Imperialist competitive algorithm for solving systems of nonlinear equations", Computers & Mathematics with Applications, 65(12), pp. 1894-1908 (2013).
27. Hosseini, S. and Al Khaled, A. "A survey on the imperialist competitive algorithm metaheuristic: Implementation in engineering domain and directions for future research", Applied Soft Computing, 24, pp. 1078-1094 (2014).
28. Sherinov, Z., Unveren, A., and Acan, A. "Imperialist competitive algorithm with updated assimilation for the solution of real valued optimization problems", International Journal on Artificial Intelligence Tools, 27(02), p. 1850005 (2018).
29. Mokhtarian Asl, M. and Sattarvand, J. "An imperialist competitive algorithm for solving the production scheduling problem in open pit mine", IJMGE Int. J. Min. & Geo-Eng., 50(1), pp. 131-143 (2016).
30. Mirjalili, S., Mirjalili, S.M., and Lewis, A. "Grey wolf optimizer", Advances in Engineering Software, 69, pp. 46-61 (2014).
31. Faris, H., Aljarah, I., Al-Betar, M.A., et al. "Grey wolf optimizer: a review of recent variants and applications", Neural Computing and Applications, 30(2), pp. 413-435 (2018).
32. Gao, Z.M. and Zhao, J. "An improved grey wolf optimization algorithm with variable weights", Computational Intelligence and Neuroscience, 2019, Article ID 2981282, 13 pages (2019). https://doi.org/10.1155/2019/2981282 (2019).
33. Yan, F., Xu, J., and Yun, K. "Dynamically dimensioned search grey wolf optimizer based on positional interaction information", Complexity, 2019, Article ID 7189653, 36 pages (2019). https://doi.org/10.1155/2019/7189653.
Volume 29, Issue 4
Transactions on Computer Science & Engineering and Electrical Engineering (D)
July and August 2022
Pages 1915-1924
  • Receive Date: 30 April 2019
  • Revise Date: 16 April 2020
  • Accept Date: 26 October 2020