Comparison of GA, SGOA, and PSO for Linear Antenna Array Optimization
DOI:
https://doi.org/10.63682/jns.v14i21S.5617Keywords:
Optimization, S LL, radiation efficiency, directivity, beam steeringAbstract
The performance of a linear antenna array is crucial in applications such as wireless communication, radar, and satellite systems. Achieving optimal radiation characteristics, including minimized sidelobe levels, enhanced directivity, and efficient beam steering, requires advanced optimization techniques. This research paper presents a comparative analysis of three prominent optimization algorithms—Genetic Algorithm (GA), Social Group Optimization Algorithm (SGOA), and Particle Swarm Optimization (PSO)—for optimizing linear antenna arrays. GA, inspired by the principles of natural selection and evolution, uses genetic operators such as selection, crossover, and mutation to explore the search space and find an optimal solution. SGOA is a social learning-based algorithm that models individual learning, social learning, and group influence to enhance convergence efficiency and solution accuracy. PSO, based on swarm intelligence, optimizes the antenna array by adjusting element excitations and positions through the collective movement of particles in a multidimensional search space. The study evaluates these algorithms based on key performance metrics such as sidelobe suppression, directivity enhancement, convergence speed, computational complexity, and radiation efficiency. Through extensive simulations, it is observed that GA effectively reduces sidelobe levels but suffers from slow convergence due to the stochastic nature of genetic operations. SGOA offers superior adaptability and faster convergence, making it suitable for real-time applications. PSO, known for its computational efficiency, demonstrates quick convergence but can struggle with local optima in highly complex optimization scenarios. The findings of this study provide valuable insights into selecting the most appropriate optimization technique based on specific antenna array design requirements. Future research can focus on hybrid approaches and machine learning-based optimizations to further improve antenna performance.
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F. J. Ares-Pena, J. A. Rodriguez-Gonzalez, E. Villanueva- Lopez and S. R. Rengarajan. (1999). “Genetic algorithms in the design and optimization of antenna array patterns,” IEEE Trans. Antennas Propagat. 47 (3),pp. 506–510.
M. G. Bray, D. H. Werner, D. W. Boeringer and D. W. Machuga. (2002). “Optimization of thinned aperiodic linear phased arrays using genetic algorithms to reduce grating lobes during scanning,” IEEE Trans. Antennas Propagat. 50 (12), pp. 1732–1742.
C. Rocha-Alicano, D. Covarrubias-Rosales, C. Brizuela- Rodriguez and M. Panduro-Mendoza. (2007). “Differential evolution algorithm applied to sidelobe level reduction on a planar array,” AEU Int. J. Electron Commun. 61 (5), pp. 286–290.
R. H. Haupt. (1994). “Thinned arrays using genetic algorithms,” IEEE Trans. Antennas Propagat. 42 (7), pp.993–999.
O. Elizarraras, A. Mendez, A. Reyna and M. A. Panduro, “Design of spherical antenna arrays for a 3D scannable pattern using differential evolution,” in Loughborough Antennas and Propag. Conf. (LAPC), Loughborough, United Kingdom, 2016, pp. 1–4.
Z. Yan-Qiu and P. Zong. “Three-dimensional phased array antenna analysis and simulation,” in 3rd IEEE Int.Symposium Microw. Antenna Propag. and EMC Technol. Wirel. Commun., Beijing, 2009, pp. 538–542.
N. Ebrahimi, A. Pirhadi and M. Karimipour. (2013). “Optimum design of shaped beam cylindrical array antenna with electronically scan radiation pattern,” Adv. Comput. Tech. Electromagn. 2013, pp. 1–11.
N. Saqib and I. Khan. (2015). “A hybrid antenna array design for 3-D direction of arrival estimation,” PloS One 10 (3), pp. e0118914.
Y. Song, “Study on radiation characteristics of a conical conformal phased array,” in Prog. in Electromagn. Res.Symposium, Cambridge, 2008, pp. 233–236.
S. Rupcic, V. Mandric and D. Zagar. (2011). “Reduction of sidelobes by nonuniform elements spacing of a spherical antenna array,” Radioengineering 20 (1), pp. 299–306.
M. Comisso and R. Vescovo. “Fast co-polar and crosspolar 3D pattern synthesis with dynamic range ratio reduction for conformal antenna arrays,” in IEEE Trans. Antennas Propag. 61 (2), 2013, pp. 614–626.
S. A. Djennas, B. Benadda, L. Merad and F. T. Bendimerad. (2014). “Conformal antennas arrays radiation synthesis using immunity tactic,” COMPEL. 33 (3), pp. 1017–1037.
Z. Zhang, J. Zhou and H. Zhang. (2015). “Full polarimetric sum and difference patterns synthesis for conformal array,” Electron. Lett. 51 (8), pp. 602–604.
14. A. Abouda, H. M. El-Sallabi and S. G. H€aggman. (2006). “Effect of antenna array geometry and ULA azimuthal orientation on MIMO channel properties in urban city street grid,” PIER. 64, pp. 257–278.
K. F. Lee, K. M. Luk, K. M. Mak, and S. L. S. Yang. (2011). “On the use of U-slots in the design of dual-and triple band patch antennas,” IEEE Antennas Propag. Mag. 53 (3), pp. 60–74.
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