Unequally Spaced Antenna Array Synthesis Using Accelerating Gaussian Mutated Cat Swarm Optimization
DOI:
https://doi.org/10.26636/jtit.2022.154821Keywords:
Gaussian mutation, cat swarm optimization, linear antenna array, PSLLAbstract
Low peak sidelobe level (PSLL) and antenna arrays with high directivity are needed nowadays for reliable wireless communication systems. Controlling the PSLL is a major issue in designing effective antenna array systems. In this paper, a nature inspired technique, namely accelerating Gaussian mutated cat swarm optimization (AGMCSO) that attributes global search abilities, is proposed to control PSLL in the radiation pattern. In AGM-SCO, Gaussian mutation with an acceleration parameter is used in the position-updated equation, which allows the algorithm to search in a systematic way to prevent premature convergence and to enhance the speed of convergence. Experiments concerning several benchmark multimodal problems have been conducted and the obtained results illustrate that AGMCSO shows excellent performance concerning evolutionary speed and accuracy. To validate the overall efficacy of the algorithm, a sensitivity analysis was performed for different AGMCSO parameters. AGMCSO was researched on numerous linear, unequally spaced antenna arrays and the results show that in terms of generating low PSLL with a narrow first null beamwidth (FNBW), AGMCSO outperforms conventional algorithms.
Downloads
References
[1] D. Khzmalyan and A. S. Kondrat'yev, „Phase-only synthesis of antenna array amplitude pattern", Int. J. Electron., vol. 81, no. 5, pp. 585-589, 1996. DOI: https://doi.org/10.1080/002072196136490
View in Google Scholar
[2] K. K. Yan and Y. Lu, „Sidelobe reduction in array pattern synthesis using genetic algorithm", IEEE Trans. Anten. Propag., vol. 45, no. 7, pp. 1117-1122, 1997. DOI: https://doi.org/10.1109/8.596902
View in Google Scholar
[3] F. J. Ares-Pena, J. A. Gonzalez, E. Lopez, and S. R. Rengarajan, „Genetic algorithms in the design and optimization of antenna array patterns", IEEE Trans. Anten. Propag., vol. 47, no. 3, pp. 506-510, 1999. DOI: https://doi.org/10.1109/8.768786
View in Google Scholar
[4] K. Chen, Z. He, and C. Han, „A modified real GA for the sparse linear array synthesis with multiple constraints", IEEE Trans. Anten. Propag., vol. 54, no. 7, pp. 2169-2173, 2006. DOI: https://doi.org/10.1109/TAP.2006.877211
View in Google Scholar
[5] L. Cen, Z. L. Yu, and W. Ser, „Linear aperiodic array synthesis using an improved genetic algorithm", IEEE Trans. Anten. Propag., vol. 60, no. 2, pp. 895-902, 2012. DOI: https://doi.org/10.1109/TAP.2011.2173111
View in Google Scholar
[6] T. Reshma, K. V. Reddy, D. Pratap, and V. Agilan, „Parameters optimization using fuzzy rule-based multi-objective genetic algorithm for an event-based Rainfall-Runoff model", Water Resour. Manag., vol. 32, no. 3, pp. 1501-1516, 2018. DOI: https://doi.org/10.1007/s11269-017-1884-2
View in Google Scholar
[7] D. G. Kurup, M. Himidi, and A. Rydberg, „Synthesis of uniform amplitude unequally spaced antenna arrays using the differentia evolution algorithm", IEEE Trans. Anten. Propag., vol. 51, no. 9, pp. 2210-2217, 2003. DOI: https://doi.org/10.1109/TAP.2003.816361
View in Google Scholar
[8] J. Q. Zhang and A. C. Sanderson, „JADE: Adaptive differential evolution with optional external archive", IEEE Trans. Evol. Comput., vol. 13, no. 5, pp. 945-958, 2009. DOI: https://doi.org/10.1109/TEVC.2009.2014613
View in Google Scholar
[9] C. Lin, A.-Y. Qing, and Q.-Y. Feng, „Synthesis of unequally spaced antenna arrays by using differential evolution", IEEE Trans. Anten. Propag., vol. 58, no. 8, pp. 2553-2561, 2010. DOI: https://doi.org/10.1109/TAP.2010.2048864
View in Google Scholar
[10] S. K. Goudos, K. Siakavara, and T. Samaras, „Sparse linear array synthesis with multiple constraints using differential evolution with strategy adaptation", IEEE Anten. Wirel. Propag. Lett., vol. 10, pp. 670-673, 2011. DOI: https://doi.org/10.1109/LAWP.2011.2161256
View in Google Scholar
[11] S. K. Goudos et al., „Self-adaptive differential evolution applied to real-valued antenna and microwave design problems", IEEE Trans. Anten. Propag., vol. 59, no. 4, pp. 1286-98, 2011. DOI: https://doi.org/10.1109/TAP.2011.2109678
View in Google Scholar
[12] F. Zhang, W. Jia, and M. Yao, „Linear aperiodic array synthesis using differential evolution algorithm", IEEE Anten. Wirel. Propag. Lett., vol. 12, pp. 797-800, 2013. DOI: https://doi.org/10.1109/LAWP.2013.2270930
View in Google Scholar
[13] D. W. Boeringer and D. H. Werner, „Particle swarm optimization versus genetic algorithms for phased array synthesis", IEEE Trans. Anten. Propag., vol. 52, no. 3, pp. 771-779, 2004. DOI: https://doi.org/10.1109/TAP.2004.825102
View in Google Scholar
[14] M. M. Khodier and C. G. Christodoulou, „Linear array geometry synthesis with minimum sidelobe level and null controlling using particle swarm optimization", IEEE Trans. Anten. Propag., vol. 53, no. 8, pp. 2674-2679, 2005. DOI: https://doi.org/10.1109/TAP.2005.851762
View in Google Scholar
[15] J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, „Comprehensive learning particle swarm optimizer for global optimization of multimodal functions", IEEE Trans. Evol. Comput., vol. 10, no. 3, pp. 281-295, 2006. DOI: https://doi.org/10.1109/TEVC.2005.857610
View in Google Scholar
[16] S. K. Goudos et al., „Application of comprehensive learning particle swarm optimizer to unequally spaced linear array synthesis with side lobe level suppression and null control", IEEE Anten. Wirel. Propag. Lett., vol. 9, pp. 125-129, 2010. DOI: https://doi.org/10.1109/LAWP.2010.2044552
View in Google Scholar
[17] W.-B. Wang, Q.-Y. Feng, and D. Liu, „Application of chaotic particle swarm optimization algorithm to pattern synthesis of antenna arrays", Progr. in Electromag. Res., vol. 115, pp. 173-189, 2011. DOI: https://doi.org/10.2528/PIER11012305
View in Google Scholar
[18] Z. H. Zhan, J. Zhang, and Y. Li, „Orthogonal learning particle swarm optimization", IEEE Trans, Evol. Comput., vol. 15, no. 6, pp. 281-295, 2011. DOI: https://doi.org/10.1109/TEVC.2010.2052054
View in Google Scholar
[19] R. Bhattacharya, T. K. Bhattacharyya, and R. Garg, „Position mutated hierarchical particle swarm optimization and its application in synthesis of unequally spaced antenna arrays", IEEE Trans. Anten. Propag., vol. 60, no. 7, pp. 3174-2181, 2012. DOI: https://doi.org/10.1109/TAP.2012.2196917
View in Google Scholar
[20] X. Xia, J. Liu, and Z. Hu, „An improved particle swarm optimizer based on tabu detecting and local learning strategy in a shrunk search space", Appl. Soft Comput., vol. 23, pp. 76-90, 2014. DOI: https://doi.org/10.1016/j.asoc.2014.06.012
View in Google Scholar
[21] L. A. Bewoor, V. C. Prakash, and S. U. Sapkal, „Evolutionary hybrid particle swarm optimization algorithm for solving np-hard no-wait ow shop scheduling problems", Algorithms, vol. 10, no. 4, pp. 1-17, 2017. DOI: https://doi.org/10.3390/a10040121
View in Google Scholar
[22] A. Ahilan et al., „Segmentation by fractional order Darwinian particle swarm optimization based multilevel thresholding and improved lossless prediction based compression algorithm for medical images", IEEE Access, vol. 7, pp. 89570-89580, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2891632
View in Google Scholar
[23] R. R. Eva and Q. T. Oscar, „Linear array synthesis using an ant colony optimization based algorithm", IEEE Trans. Anten. Propag., vol. 49, no. 2, pp. 70-79, 2007. DOI: https://doi.org/10.1109/MAP.2007.376644
View in Google Scholar
[24] M. S. Kumar and M. Venkatesan, „Multi-objective task scheduling using hybrid genetic ant colony optimization algorithm in cloud environment", Wirel. Pers. Commun., vol 107, pp. 1835-1848, 2019. DOI: https://doi.org/10.1007/s11277-019-06360-8
View in Google Scholar
[25] C. J. Malar et al., „Multi constraints applied energy effcient routing technique based on ant colony optimization used for disaster resilient location detection in mobile ad-hoc network", J. of Ambient Intell. and Human. Comput., vol. 12, pp. 4007-4017, 2021. DOI: https://doi.org/10.1007/s12652-020-01767-9
View in Google Scholar
[26] G. Panda, P. M. Pradhan, and B. Majhi, „IIR system identification using cat swarm optimization", Expert Syst. Appl.: an Int. J., vol. 38, no. 10, pp. 12671-12683, 2011. DOI: https://doi.org/10.1016/j.eswa.2011.04.054
View in Google Scholar
[27] P.-W. Tsai, J.-S. Pan, S. M. Chen, and B. Y. Liao, „Enhanced paralel cat swarm optimization based on the Taguchi method", Expert Syst. with Appl., vol. 39, no. 7, pp. 6309-6319, 2012. DOI: https://doi.org/10.1016/j.eswa.2011.11.117
View in Google Scholar
[28] L. Pappula and D. Ghosh, „Linear antenna array synthesis using cat swarm optimization", AEU - Int. J. Electron. Commun., vol. 68, no. 6, pp. 540-549, 2014. DOI: https://doi.org/10.1016/j.aeue.2013.12.012
View in Google Scholar
[29] J. Wang, „A new cat swarm optimization with adaptive parameter control", in Genetic and Evolutionary Computing: Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18-20, 2014, Nanchang, China, H. Sun et al., Eds. AISC, vol. 329, pp. 69-78. Springer, 2015. DOI: https://doi.org/10.1007/978-3-319-12286-1_8
View in Google Scholar
[30] T. Nireekshana, G. Kesava Rao, and S. Sivanaga Raju, „Available transfer capability enhancement with FACTS using cat swarm optimization", Ain Shams Engin. J., vol. 7, no. 1, pp. 159-167, 2016. DOI: https://doi.org/10.1016/j.asej.2015.11.011
View in Google Scholar
[31] L. Pappula and D. Ghosh, „Synthesis of linear aperiodic array using Cauchy mutated cat swarm optimization", AEU - Int. J. of Electron. and Commun., vol. 72, pp. 52-64, 2017. DOI: https://doi.org/10.1016/j.aeue.2016.11.016
View in Google Scholar
[32] L. Pappula and D. Ghosh, „Cat swarm optimization with normal mutation for fast convergence of multimodal functions", Appl. Soft Comput., vol. 66, pp. 473-491, 2018. DOI: https://doi.org/10.1016/j.asoc.2018.02.012
View in Google Scholar
[33] R. Rajakumar, J. Amudhavel, P. Dhavachelvan, and T. Vengattaraman, „GWO-LPWSN: Grey Wolf optimization algorithm for node localization problem in wireless sensor networks", J. of Comp. Netw. and Commun., vol. 2, pp. 1-10, 2017. DOI: https://doi.org/10.1155/2017/7348141
View in Google Scholar
[34] X. Li and K. M. Luk, „The grey wolf optimizer and its applications in electromagnetics", IEEE Trans. on Antenn. and Propag., vol. 68, no. 3, 2020. DOI: https://doi.org/10.1109/TAP.2019.2938703
View in Google Scholar
[35] M. Rajeswari, J. Amudhavel, S. Pothula, and P. Dhavachelvan, "Directed bee colony optimization algorithm to solve the nurse rostering problem", Computat. Intell. and Neurosci., vol. 2017, Article ID 6563498, 2017 [Online]. Available: https://downloads.hindawi.com/journals/cin/2017/6563498.pdf DOI: https://doi.org/10.1155/2017/6563498
View in Google Scholar
[36] R. S. Raghav, J. Amudhavel, and P. Dhavachelvan, „Enhanced artificial bee colony optimization for solving economic load dispatch, IIOAB J., vol. 8, no. 2, pp. 208-216, 2017 [Online]. Available: https://www.iioab.org/IIOABJ 8.2 208-216.pdf
View in Google Scholar
[37] S.-C. Chu and P.-W. Tsai, „Computational intelligence based on the behavior of cats", Int. J. Innov. Comput., Inform. and Contr. (IJICIC), vol. 3, no. 1, pp. 163-173, 2007 [Online]. Available: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.103.3698&rep=rep1&type=pdf
View in Google Scholar
[38] X. Yao, Y. Liu, and G. M. Lin, „Evolutionary programming made faster", IEEE Trans. Evol. Comput., vol. 3, no. 2, pp. 82-102, 1999. DOI: https://doi.org/10.1109/4235.771163
View in Google Scholar
Downloads
Submitted
Published
Issue
Section
License
Copyright (c) 2022 Journal of Telecommunications and Information Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.