Circular Antenna Array Synthesis Using Salp Swarm Optimization

Year 2020, Volume: 8 Issue: 4, 320 - 324, 30.10.2020

Ali Durmuş

https://doi.org/10.17694/bajece.713444

Cited By: 2

Abstract

Salp Swarm Algorithm (SSA) is used to design an optimal non uniform circular antenna arrays. Salp Swarm Algorithm which mimics the swarming behavior of salps in oceans is a nature-inspired optimization method. As it is simple and easy to apply, it has been applied to many different problems in the literature. SSA method optimally determined the positions and amplitudes of the circular antenna array elements to obtain radiation patterns with a low maximum sidelobe level (MSL) and narrow half power beam width (HPBW). Different sizes of antennas with 8, 10 and 12 elements are discussed to demonstrate the capability of the SSA algorithm. The results of MSL and HPBW obtained by SSA for the synthesis of circular antenna arrays are better than other compared optimization methods.

Keywords

Salp swarm algorithm, synthesis of antenna array, circular arrays

References

• C. A. Balanis, Antenna theory: analysis and design, 3rd edn. Wiley, New York, 2005.
• J. R. Mailloux, Phased array antenna handbook. Artech House, Boston, 2005.
• M. A. Panduro, A. L. Mendez, R. Dominguez, G. Romero. "Design of non-uniform circular antenna arrays for side lobe reduction using the method of genetic algorithms." AEU Int. J. Electron Commun., vol.60.7, 2006, pp. 713–717.
• M. Shihab, Y. Najjar, N. Khodier. "Design of non-uniform circular antenna arrays using particle swarm optimization." J. Electr. Eng., vol. 59, 2008, pp. 216–220.
• A. Sharaqa, N. Dib. "Circular antenna array synthesis using firefly algorithm." Int. J. RF Microw. Comput. Eng., vol.24, 2014, pp. 139–146.
• K. Guney, S. Basbug. "A parallel implementation of seeker optimization algorithm for designing circular and concentric circular antenna arrays." Appl. Soft Comput. Vol.22, 2014, pp. 287–296.
• B. Babayigit, E. Senyigit. "Design optimization of circular antenna arrays using Taguchi method." Neural Computing & Application, vol. 28, 2017, pp.1443-1452.
• S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, S. M. Mirjalili. "Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems." Advances in Engineering Software, vol. 114, 2017, pp. 163-191.
• H. Faris, M. Mafarja, A. Heidari, I. Aljarah, A. Al-Zoubi, S. Mirjalili, et al. "An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. " Knowledge-Based Systems, vol. 154, 2018, pp. 43–67.
• R. Abbassi, A. Abbassi, A. Heidari, S. Mirjalili. "An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models." Energy Conversion and Management, vol. 179, 2019, pp. 362–372.
• E. E. Elattar, S. K. ElSayed. "Probabilistic energy management with emission of renewable micro-grids including storage devices based on efficient salp swarm algorithm." Renewable Energy, vol. 153, 2020, pp. 23-35.
• X. Zhao, P. Wu, X. Yin. "A quadratic penalty item optimal variational mode decomposition method based on single-objective salp swarm algorithm." Mechanical Systems and Signal Processing, vol. 138, 2020, p. 106567.
• W. Liu, R. Wang, J. Su. "An Image Impulsive Noise Denoising Method Based on Salp Swarm Algorithm." International Journal of Education and Management Engineering, vol. 10, 2020, p. 43.