{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T20:04:13Z","timestamp":1775073853080,"version":"3.50.1"},"reference-count":146,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European University of Atlantic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO.<\/jats:p>","DOI":"10.3390\/s23187710","type":"journal-article","created":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T10:23:42Z","timestamp":1693995822000},"page":"7710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants"],"prefix":"10.3390","volume":"23","author":[{"given":"Arooj","family":"Khan","sequence":"first","affiliation":[{"name":"College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"}]},{"given":"Imran","family":"Shafi","sequence":"additional","affiliation":[{"name":"College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"}]},{"given":"Sajid Gul","family":"Khawaja","sequence":"additional","affiliation":[{"name":"College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3134-7720","authenticated-orcid":false,"given":"Isabel","family":"de la Torre D\u00edez","sequence":"additional","affiliation":[{"name":"Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5524-5366","authenticated-orcid":false,"given":"Miguel Angel L\u00f3pez","family":"Flores","sequence":"additional","affiliation":[{"name":"Research Group on Foods, Universidad Europea del Atl\u00e1ntico, Isabel Torres 21, 39011 Santander, Spain"},{"name":"Research Group on Foods, Universidad Internacional Iberoamericana, Campeche 24560, Mexico"},{"name":"Instituto Polit\u00e9cnico Nacional, UPIICSA, Ciudad de Mexico 04510, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7156-9585","authenticated-orcid":false,"given":"Juan Casta\u00f1edo","family":"Galvl\u00e1n","sequence":"additional","affiliation":[{"name":"Research Group on Foods, Universidad Europea del Atl\u00e1ntico, Isabel Torres 21, 39011 Santander, Spain"},{"name":"Universidad Internacional Iberoamericana Arecibo, Arecibo, PR 00613, USA"},{"name":"Department of Projects, Universidade Internacional do Cuanza, Cuito EN250, Bi\u00e9, Angola"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"ref_1","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Djaneye-Boundjou, O.S.E. (2013). Particle Swarm Optimization Stability Analysis. [Doctoral\u2019s Dissertation, University of Dayton].","DOI":"10.1109\/ICCAS.2013.6703971"},{"key":"ref_3","first-page":"8","article-title":"Particle swarm optimization","volume":"2","author":"Shi","year":"2004","journal-title":"IEEE Connect."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6214","DOI":"10.1109\/TAP.2013.2281517","article-title":"Particle swarm optimization of microstrip antennas for wireless communication systems","volume":"61","author":"Minasian","year":"2013","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/s11047-007-9049-5","article-title":"A review of particle swarm optimization. Part I: Background and development","volume":"6","author":"Banks","year":"2007","journal-title":"Nat. Comput."},{"key":"ref_6","unstructured":"Parsopoulos, K.E., and Vrahatis, M.N. (2008). Multi-Objective Optimization in Computational Intelligence: Theory and Practice, IGI Global."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Vaseghi, S.V. (2008). Advanced Digital Signal Processing and Noise Reduction, John Wiley & Sons.","DOI":"10.1002\/9780470740156"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/48.289455","article-title":"Phase-coherent digital communications for underwater acoustic channels","volume":"19","author":"Stojanovic","year":"1994","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_9","unstructured":"Gustafsson, R. (2003). Combating Intersymbol Interference and Cochannel Interference in Wireless Communication Systems. [Ph.D. Thesis, Blekinge Institute of Technology]."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/JSSC.2002.808282","article-title":"An adaptive PAM-4 5-Gb\/s backplane transceiver in 0.25-\u03bcm CMOS","volume":"38","author":"Stonick","year":"2003","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/COMST.2018.2878035","article-title":"Classifications and applications of physical layer security techniques for confidentiality: A comprehensive survey","volume":"21","author":"Hamamreh","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1109\/TCSI.2013.2283675","article-title":"A 5-Gb\/s serial-link redriver with adaptive equalizer and transmitter swing enhancement","volume":"61","author":"Liu","year":"2013","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/PROC.1985.13298","article-title":"Adaptive equalization","volume":"73","author":"Qureshi","year":"1985","journal-title":"Proc. IEEE"},{"key":"ref_14","first-page":"2958","article-title":"Introduction to robust, reliable, and high-speed power-line communication systems","volume":"84","author":"Katayama","year":"2001","journal-title":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101489","DOI":"10.1016\/j.phycom.2021.101489","article-title":"Uplink data transmission for high speed trains in severe doubly selective channels of 6G communication systems","volume":"49","author":"Vahidi","year":"2021","journal-title":"Phys. Commun."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rojo-\u00c1lvarez, J.L., Mart\u00ednez-Ram\u00f3n, M., Munoz-Mari, J., and Camps-Valls, G. (2018). Digital Signal Processing with Kernel Methods, John Wiley & Sons.","DOI":"10.1002\/9781118705810"},{"key":"ref_17","unstructured":"Ali, S., Saad, W., Rajatheva, N., Chang, K., Steinbach, D., Sliwa, B., Wietfeld, C., Mei, K., Shiri, H., and Zepernick, H.J. (2020). 6G white paper on machine learning in wireless communication networks. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1109\/JOE.2015.2469895","article-title":"Robust equalization of mobile underwater acoustic channels","volume":"40","author":"Pelekanakis","year":"2015","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_19","unstructured":"Stojanovic, M., and Beaujean, P.P.J. (2016). Springer Handbook of Ocean Engineering, Springer."},{"key":"ref_20","first-page":"361","article-title":"Multicarrier communication for underwater acoustic channel","volume":"6","author":"Esmaiel","year":"2013","journal-title":"Int\u2019L Commun. Netw. Syst. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1016\/j.sigpro.2011.12.012","article-title":"Low-complexity adaptive decision-feedback equalization of MIMO channels","volume":"92","author":"Arablouei","year":"2012","journal-title":"Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/j.1538-7305.1966.tb00020.x","article-title":"Techniques for adaptive equalization of digital communication systems","volume":"45","author":"Lucky","year":"1966","journal-title":"Bell Syst. Tech. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2520","DOI":"10.11591\/ijece.v7i5.pp2520-2529","article-title":"LMS adaptive filters for noise cancellation: A review","volume":"7","author":"Dixit","year":"2017","journal-title":"Int. J. Electr. Comput. Eng. (IJECE)"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1109\/TSP.2012.2236831","article-title":"A family of shrinkage adaptive-filtering algorithms","volume":"61","author":"Bhotto","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9385","DOI":"10.1007\/s13369-020-05264-1","article-title":"Fractional LMS and NLMS algorithms for line echo cancellation","volume":"46","author":"Khan","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4698","DOI":"10.1016\/j.ijleo.2016.02.005","article-title":"Analysis on the adaptive filter based on LMS algorithm","volume":"127","author":"Zhu","year":"2016","journal-title":"Optik"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ao, W., Xiang, W.Q., Zhang, Y.P., Wang, L., Lv, C.Y., and Wang, Z.H. (2012, January 23\u201325). A new variable step size LMS adaptive filtering algorithm. Proceedings of the 2012 International Conference on Computer Science and Electronics Engineering, Hangzhou, China.","DOI":"10.1109\/ICCSEE.2012.115"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Atapattu, L., Arachchige, G.M., Ziri-Castro, K., Suzuki, H., and Jayalath, D. (2012, January 7\u20139). Linear adaptive channel equalization for multiuser MIMO-OFDM systems. Proceedings of the Australasian Telecommunication Networks and Applications Conference (ATNAC) 2012, Brisbane, QLD, Australia.","DOI":"10.1109\/ATNAC.2012.6398080"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15623\/ijret.2013.0210001","article-title":"Implementation of Adaptive Filter Based on LMS Algorithm","volume":"2","author":"Reddy","year":"2013","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_30","unstructured":"Douglas, S.C. (2017). Digital Signal Processing Fundamentals, Nova Science Publishers."},{"key":"ref_31","unstructured":"Jaar, F.Y.S. (2019). Recursive Inverse Adaptive Filtering for Fading Communication Channels. [Master\u2019s Thesis, Eastern Mediterranean University (EMU)-Do\u011fu Akdeniz \u00dcniversitesi (DA\u00dc)]."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Khokhar, M.J., and Younis, M.S. (2012, January 21\u201325). Development of the RLS algorithm based on the iterative equation solvers. Proceedings of the 2012 IEEE 11th International Conference on Signal Processing, Beijing, China.","DOI":"10.1109\/ICoSP.2012.6491653"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, S., and Zhang, J. (2015, January 14\u201318). An RLS algorithm with evolving forgetting factor. Proceedings of the 2015 Seventh International Workshop on Signal Design and its Applications in Communications (IWSDA), Bengaluru, India.","DOI":"10.1109\/IWSDA.2015.7458406"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Claser, R., Nascimento, V.H., and Zakharov, Y.V. (September, January 29). A low-complexity RLS-DCD algorithm for Volterra system identification. Proceedings of the 2016 24th European Signal Processing Conference (EUSIPCO), Budapest, Hungary.","DOI":"10.1109\/EUSIPCO.2016.7760199"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102934","DOI":"10.1016\/j.mechatronics.2022.102934","article-title":"Filtered-error RLS for self-tuning disturbance feedforward control with application to a multi-axis vibration isolator","volume":"89","author":"Hakvoort","year":"2023","journal-title":"Mechatronics"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","article-title":"Particle swarm optimization algorithm: An overview","volume":"22","author":"Wang","year":"2018","journal-title":"Soft Comput."},{"key":"ref_37","unstructured":"Bansal, S., Ali, H., and Singh, S. (2014). Performing Adaptive Channel Equalization by Hybrid Differential Evolution Particle Swarm Optimization. Int. Res. Appl. Sci. Eng. Tech., 2."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1109\/TMAG.2006.871426","article-title":"A particle swarm optimization method with enhanced global search ability for design optimizations of electromagnetic devices","volume":"42","author":"Ho","year":"2006","journal-title":"IEEE Trans. Magn."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Sun, A., Loh, H.T., Lu, W.F., and Lim, E.P. (2008). Advances of Computational Intelligence in Industrial Systems, Springer.","DOI":"10.1007\/978-3-540-78297-1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rao, R.V., Savsani, V.J., Rao, R.V., and Savsani, V.J. (2012). Advanced Optimization Techniques, Springer.","DOI":"10.1007\/978-1-4471-2748-2_2"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Vikhar, P.A. (2016, January 22\u201324). Evolutionary algorithms: A critical review and its future prospects. Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, India.","DOI":"10.1109\/ICGTSPICC.2016.7955308"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1109\/TNET.2019.2907243","article-title":"Robustness optimization scheme with multi-population co-evolution for scale-free wireless sensor networks","volume":"27","author":"Qiu","year":"2019","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_43","first-page":"12682","article-title":"Performance analysis of adaptive channel equalizer using LMS, various architecture of ANN and GA","volume":"12","author":"Kundu","year":"2017","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xu, W., Zhong, Z., Be\u2019ery, Y., You, X., and Zhang, C. (2018, January 28\u201331). Joint neural network equalizer and decoder. Proceedings of the 2018 15th International Symposium on Wireless Communication Systems (ISWCS), Lisbon, Portugal.","DOI":"10.1109\/ISWCS.2018.8491056"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"125272","DOI":"10.1016\/j.optcom.2020.125272","article-title":"Deep neural network method for channel estimation in visible light communication","volume":"462","author":"Wu","year":"2020","journal-title":"Opt. Commun."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mathews, A.B., Mathews, A.B., and Agees Kumar, C. (2023). A Non-Linear Improved CNN Equalizer with Batch Gradient Decent in 5G Wireless Optical Communication. IETE J. Res., 1\u201313.","DOI":"10.1080\/03772063.2022.2163930"},{"key":"ref_47","unstructured":"Erpek, T., O\u2019Shea, T.J., Sagduyu, Y.E., Shi, Y., and Clancy, T.C. (2020). Development and Analysis of Deep Learning Architectures, Springer."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/CC.2017.8233654","article-title":"Deep learning for wireless physical layer: Opportunities and challenges","volume":"14","author":"Wang","year":"2017","journal-title":"China Commun."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3947","DOI":"10.1007\/s10462-019-09784-7","article-title":"A survey of regularization strategies for deep models","volume":"53","author":"Moradi","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_50","first-page":"8887","article-title":"Optimization of lms algorithm for adaptive filtering using global optimization techniques","volume":"975","author":"Tripathi","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.ins.2012.10.012","article-title":"Diversity enhanced particle swarm optimization with neighborhood search","volume":"223","author":"Wang","year":"2013","journal-title":"Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","article-title":"Deep learning applications and challenges in big data analytics","volume":"2","author":"Najafabadi","year":"2015","journal-title":"J. Big Data"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.chemolab.2015.08.020","article-title":"Particle swarm optimization (PSO). A tutorial","volume":"149","author":"Marini","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_54","first-page":"50","article-title":"Comparative study of firefly algorithm and particle swarm optimization for noisy nonlinear optimization problems","volume":"4","author":"Pal","year":"2012","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1016\/j.asoc.2007.10.007","article-title":"A distributed PSO\u2013SVM hybrid system with feature selection and parameter optimization","volume":"8","author":"Huang","year":"2008","journal-title":"Appl. Soft Comput."},{"key":"ref_56","first-page":"65","article-title":"Comparing nonlinear inertia weights and constriction factors in particle swarm optimization","volume":"15","author":"Tuppadung","year":"2011","journal-title":"Int. J. Knowl.-Based Intell. Eng. Syst."},{"key":"ref_57","unstructured":"Eberhart, R.C., and Shi, Y. (2000, January 16\u201319). Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 congress on evolutionary computation. CEC00 (Cat. No. 00TH8512), La Jolla, CA, USA."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TSMCB.2009.2015956","article-title":"Adaptive particle swarm optimization","volume":"39","author":"Zhan","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_59","unstructured":"Urade, H.S., and Patel, R. (2011, January 31\u201333). Study and analysis of particle swarm optimization: A review. Proceedings of the IJCA Proceedings on 2nd National Conference on Information and Communication Technology NCICT (4)."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Du, K.L., Swamy, M., Du, K.L., and Swamy, M. (2016). Search and Optimization by Metaheuristics: Techniques and Algorithms Inspired by Nature, IEEE.","DOI":"10.1007\/978-3-319-41192-7"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Das, S., Konar, A., and Chakraborty, U.K. (2005, January 25\u201329). Two improved differential evolution schemes for faster global search. Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, Washington, DC, USA.","DOI":"10.1145\/1068009.1068177"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1109\/TCYB.2015.2474153","article-title":"Particle swarm optimization with interswarm interactive learning strategy","volume":"46","author":"Qin","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_63","first-page":"1","article-title":"Automatic clustering using an improved particle swarm optimization","volume":"1","author":"Kuo","year":"2013","journal-title":"J. Ind. Intell. Inf."},{"key":"ref_64","first-page":"724","article-title":"Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: A case study in Talesh, Northern Iran","volume":"12","author":"Kazempour","year":"2018","journal-title":"Eng. Appl. Comput. Fluid Mech."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Cuevas, E., Fausto, F., Gonz\u00e1lez, A., Cuevas, E., Fausto, F., and Gonz\u00e1lez, A. (2020). A swarm algorithm inspired by the collective animal behavior. New Advancements in Swarm Algorithms: Operators and Applications, Springer.","DOI":"10.1007\/978-3-030-16339-6"},{"key":"ref_66","first-page":"466","article-title":"Performance comparison of modified LMS and RLS algorithms in de-noising of ECG signals","volume":"2","author":"Islam","year":"2012","journal-title":"Int. J. Eng. Technol"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"074001","DOI":"10.1088\/1361-6633\/aab406","article-title":"Machine learning & artificial intelligence in the quantum domain: A review of recent progress","volume":"81","author":"Dunjko","year":"2018","journal-title":"Rep. Prog. Phys."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Variddhisa\u00ef, T., and Mandic, D.P. (2017, January 23\u201325). On an RLS-like LMS adaptive filter. Proceedings of the 2017 22nd International Conference on Digital Signal Processing (DSP), London, UK.","DOI":"10.1109\/ICDSP.2017.8096130"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Mahmutoglu, Y., Turk, K., and Tugcu, E. (2016, January 27\u201329). Particle swarm optimization algorithm based decision feedback equalizer for underwater acoustic communication. Proceedings of the 2016 39th International Conference on Telecommunications and Signal Processing (TSP), Vienna, Austria.","DOI":"10.1109\/TSP.2016.7760848"},{"key":"ref_70","unstructured":"Xu, J., Yue, X., and Xin, Z. (2005, January 6\u20139). A real application of extended particle swarm optimizer. Proceedings of the 2005 5th International Conference on Information Communications & Signal Processing, Bangkok, Thailand."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.asoc.2007.01.010","article-title":"On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems","volume":"8","author":"Arumugam","year":"2008","journal-title":"Appl. Soft Comput."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1007\/s11831-021-09694-4","article-title":"Particle swarm optimization algorithm and its applications: A systematic review","volume":"29","author":"Gad","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"100868","DOI":"10.1016\/j.swevo.2021.100868","article-title":"Major advances in particle swarm optimization: Theory, analysis, and application","volume":"63","author":"Houssein","year":"2021","journal-title":"Swarm Evol. Comput."},{"key":"ref_74","unstructured":"Bonyadi, M. (2015). Particle Swarm Optimization: Theoretical ANALYSIS, modifications, and Applications to Constrained Optimization Problems. [Ph.D. Thesis, University of Adelaide]."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"3308","DOI":"10.1007\/s10489-019-01448-x","article-title":"Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing","volume":"49","author":"Mapetu","year":"2019","journal-title":"Appl. Intell."},{"key":"ref_76","unstructured":"Sohail, M.S., Saeed, M.O.B., Rizvi, S.Z., Shoaib, M., and Sheikh, A.U.H. (2014). Low-complexity particle swarm optimization for time-critical applications. arXiv."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Asif, M., Khan, M.A., Abbas, S., and Saleem, M. (2019, January 30\u201331). Analysis of space & time complexity with PSO based synchronous MC-CDMA system. Proceedings of the 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan.","DOI":"10.1109\/ICOMET.2019.8673401"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Li, X., and Engelbrecht, A.P. (2007, January 7\u201311). Particle swarm optimization: An introduction and its recent developments. Proceedings of the 9th Annual Conference Companion on Genetic and Evolutionary Computation, London, UK.","DOI":"10.1145\/1274000.1274118"},{"key":"ref_79","unstructured":"Parsopoulos, K.E., and Vrahatis, M.N. (2010). Advances and Applications: Advances and Applications, Information Science (IGI)."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"3096","DOI":"10.1016\/j.ins.2008.01.020","article-title":"Multi-strategy ensemble particle swarm optimization for dynamic optimization","volume":"178","author":"Du","year":"2008","journal-title":"Inf. Sci."},{"key":"ref_81","unstructured":"Hu, X., Shi, Y., and Eberhart, R. (2004, January 19\u201323). Recent advances in particle swarm. Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753), Portland, OR, USA."},{"key":"ref_82","unstructured":"Eberhart, R., and Kennedy, J. (1995, January 4\u20136). A new optimizer using particle swarm theory. Proceedings of the MHS\u201995: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan."},{"key":"ref_83","first-page":"35","article-title":"New particle swarm optimizer with sigmoid increasing inertia weight","volume":"1","author":"Malik","year":"2007","journal-title":"Int. J. Comput. Sci. Secur."},{"key":"ref_84","first-page":"214","article-title":"Particle swarm optimization method for constrained optimization problems","volume":"76","author":"Parsopoulos","year":"2002","journal-title":"Intell. Technol.-Appl. New Trends Intell. Technol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/TSMCB.2011.2167966","article-title":"An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization","volume":"42","author":"Islam","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/s00170-006-0719-8","article-title":"Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization","volume":"35","author":"Karpat","year":"2007","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_87","first-page":"129","article-title":"Particle swarm and ant colony algorithms hybridized for improved continuous optimization","volume":"188","author":"Shelokar","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"ref_88","first-page":"1772","article-title":"Comparing particle swarms for tracking extrema in dynamic environments","volume":"Volume 3","author":"Li","year":"2003","journal-title":"Proceedings of the CEC\u201903: The 2003 Congress on Evolutionary Computation"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10462-010-9191-9","article-title":"A review on particle swarm optimization algorithms and their applications to data clustering","volume":"35","author":"Rana","year":"2011","journal-title":"Artif. Intell. Rev."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1002\/nme.1646","article-title":"Parallel asynchronous particle swarm optimization","volume":"67","author":"Koh","year":"2006","journal-title":"Int. J. Numer. Methods Eng."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1016\/j.renene.2022.05.123","article-title":"A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems","volume":"194","author":"Alabi","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Nepomuceno, F.V., and Engelbrecht, A.P. (2013, January 20\u201323). A self-adaptive heterogeneous pso for real-parameter optimization. Proceedings of the 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico.","DOI":"10.1109\/CEC.2013.6557592"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1550147718761583","DOI":"10.1177\/1550147718761583","article-title":"Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks","volume":"14","author":"Gao","year":"2018","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.neucom.2017.11.077","article-title":"Feature selection in machine learning: A new perspective","volume":"300","author":"Cai","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"104015","DOI":"10.1016\/j.scs.2022.104015","article-title":"Decision-making and optimal design of green energy system based on statistical methods and artificial neural network approaches","volume":"84","author":"Samy","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_96","first-page":"35","article-title":"Deep learning for sustainable asset management decision-making","volume":"24","author":"Cherrington","year":"2021","journal-title":"Int. J. COMADEM"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"115870","DOI":"10.1016\/j.eswa.2021.115870","article-title":"An efficient slime mould algorithm for solving multi-objective optimization problems","volume":"187","author":"Houssein","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_98","unstructured":"Sonti, V.K., and Sundari, G. (2021). Intelligent Paradigms for Smart Grid and Renewable Energy Systems, Springer."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.swevo.2018.07.002","article-title":"Global genetic learning particle swarm optimization with diversity enhancement by ring topology","volume":"44","author":"Lin","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/TEVC.2017.2754271","article-title":"A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems","volume":"22","author":"Yue","year":"2017","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_101","unstructured":"Wang, Y.X., and Xiang, Q.L. (2008, January 1\u20136). Particle swarms with dynamic ring topology. Proceedings of the 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, China."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"74","DOI":"10.13164\/mendel.2021.2.074","article-title":"Relation of neighborhood size and diversity loss rate in particle swarm optimization with ring topology","volume":"27","author":"Kazikova","year":"2021","journal-title":"Mendel"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Borowska, B. (2020, January 3\u20135). Genetic learning particle swarm optimization with interlaced ring topology. Proceedings of the Computational Science\u2013ICCS 2020: 20th International Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-030-50426-7_11"},{"key":"ref_104","unstructured":"Liang, J.J., and Suganthan, P.N. (2005, January 8\u201312). Dynamic multi-swarm particle swarm optimizer. Proceedings of the SIS 2005: Proceedings 2005 IEEE Swarm Intelligence Symposium, Pasadena, CA, USA."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Li, Z.J., Liu, X.D., and Duan, X.D. (2008, January 18\u201320). A Particle Swarm Algorithm Based on Stochastic Evolutionary Dynamics. Proceedings of the 2008 Fourth International Conference on Natural Computation, Jinan, China.","DOI":"10.1109\/ICNC.2008.103"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"116301","DOI":"10.1016\/j.eswa.2021.116301","article-title":"Fitness peak clustering based dynamic multi-swarm particle swarm optimization with enhanced learning strategy","volume":"191","author":"Tao","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1007\/s00607-019-00782-9","article-title":"Dynamic multi-swarm global particle swarm optimization","volume":"102","author":"Xia","year":"2020","journal-title":"Computing"},{"key":"ref_108","unstructured":"Liang, J.J., and Suganthan, P.N. (2006, January 16\u201321). Dynamic multi-swarm particle swarm optimizer with a novel constraint-handling mechanism. Proceedings of the 2006 IEEE International Conference on Evolutionary Computation, Vancouver, BC, Canada."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1016\/j.asoc.2017.08.051","article-title":"A novel multi-swarm particle swarm optimization with dynamic learning strategy","volume":"61","author":"Ye","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Montes de Oca, M.A., and St\u00fctzle, T. (2008, January 12\u201316). Convergence behavior of the fully informed particle swarm optimization algorithm. Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, Atlanta, GA, USA.","DOI":"10.1145\/1389095.1389106"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Mansour, E.M., and Ahmadi, A. (2019, January 10\u201313). A novel clustering algorithm based on fully-informed particle swarm. Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand.","DOI":"10.1109\/CEC.2019.8790086"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Cleghorn, C.W., and Engelbrecht, A. (2015, January 25\u201328). Fully informed particle swarm optimizer: Convergence analysis. Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan.","DOI":"10.1109\/CEC.2015.7256888"},{"key":"ref_113","unstructured":"Hu, X., and Eberhart, R. (2002, January 12\u201317). Multiobjective optimization using dynamic neighborhood particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation. CEC\u201902 (Cat. No. 02TH8600), Honolulu, HI, USA."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"9290","DOI":"10.1109\/TCYB.2020.3029748","article-title":"A dynamic neighborhood-based switching particle swarm optimization algorithm","volume":"52","author":"Zeng","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ins.2012.04.028","article-title":"A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization","volume":"209","author":"Nasir","year":"2012","journal-title":"Inf. Sci."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"117713","DOI":"10.1016\/j.eswa.2022.117713","article-title":"A dynamic neighborhood balancing-based multi-objective particle swarm optimization for multi-modal problems","volume":"205","author":"Gu","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_117","first-page":"5208","article-title":"Particle swarm optimization: Hybridization perspectives and experimental illustrations","volume":"217","author":"Thangaraj","year":"2011","journal-title":"Appl. Math. Comput."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1007\/s13369-018-3387-8","article-title":"A hybrid particle swarm optimization technique for adaptive equalization","volume":"44","author":"Khan","year":"2019","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_119","first-page":"60","article-title":"Search optimization using hybrid particle sub-swarms and evolutionary algorithms","volume":"6","author":"Grosan","year":"2005","journal-title":"Int. J. Simul. Syst. Sci. Technol."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1109\/TPWRS.2005.846049","article-title":"A hybrid particle swarm optimization applied to loss power minimization","volume":"20","author":"Esmin","year":"2005","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.ins.2014.09.030","article-title":"Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems","volume":"293","author":"Li","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1016\/j.engappai.2011.05.010","article-title":"A multi-swarm self-adaptive and cooperative particle swarm optimization","volume":"24","author":"Zhang","year":"2011","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_123","first-page":"861","article-title":"Knowledge-based cooperative particle swarm optimization","volume":"205","author":"Jie","year":"2008","journal-title":"Appl. Math. Comput."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1109\/TSMCB.2005.850530","article-title":"A hierarchical particle swarm optimizer and its adaptive variant","volume":"35","author":"Janson","year":"2005","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_125","unstructured":"Janson, S., and Middendorf, M. (2003, January 8\u201312). A hierarchical particle swarm optimizer. Proceedings of the 2003 Congress on Evolutionary Computation, 2003-CEC\u201903, Canberra, ACT, Australia."},{"key":"ref_126","unstructured":"Janson, S., and Middendorf, M. (2004, January 5\u20137). A hierarchical particle swarm optimizer for dynamic optimization problems. Proceedings of the Applications of Evolutionary Computing: EvoWorkshops 2004: EvoBIO, EvoCOMNET, EvoHOT, EvoISAP, EvoMUSART, and EvoSTOC, Coimbra, Portugal."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s10710-006-9014-6","article-title":"A hierarchical particle swarm optimizer for noisy and dynamic environments","volume":"7","author":"Janson","year":"2006","journal-title":"Genet. Program. Evolvable Mach."},{"key":"ref_128","first-page":"1524","article-title":"Hierarchical particle swarm optimization-incorporated latent factor analysis for large-scale incomplete matrices","volume":"8","author":"Chen","year":"2021","journal-title":"IEEE Trans. Big Data"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1109\/TPWRS.2008.926455","article-title":"Self-organizing hierarchical particle swarm optimization for nonconvex economic dispatch","volume":"23","author":"Chaturvedi","year":"2008","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.asoc.2009.08.038","article-title":"Comprehensive learning particle swarm optimization for reactive power dispatch","volume":"10","author":"Mahadevan","year":"2010","journal-title":"Appl. Soft Comput."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TEVC.2018.2885075","article-title":"Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions","volume":"23","author":"Cao","year":"2018","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Yogi, S., Subhashini, K., Satapathy, J., and Kumar, S. (2010, January 7\u20139). Equalization of digital communication channels based on PSO algorithm. Proceedings of the 2010 International Conference on Communication Control and Computing Technologies, Nagercoil, India.","DOI":"10.1109\/ICCCCT.2010.5670744"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.dsp.2010.05.001","article-title":"A new modified particle swarm optimization algorithm for adaptive equalization","volume":"21","author":"Zerguine","year":"2011","journal-title":"Digit. Signal Process."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Sahu, J., and Majumder, S. (2021, January 5\u20137). A Particle Swarm Optimization based Training Algorithm for MCMA Blind Adaptive Equalizer. Proceedings of the 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India.","DOI":"10.1109\/ESCI50559.2021.9396935"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Yogi, S., Subhashini, K., and Satapathy, J. (August, January 29). A PSO based functional link artificial neural network training algorithm for equalization of digital communication channels. Proceedings of the 2010 5th International Conference on Industrial and Information Systems, Mangalore, India.","DOI":"10.1109\/ICIINFS.2010.5578726"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1007\/s11277-019-06699-y","article-title":"A PSO-based hybrid adaptive equalization algorithm for asynchronous cooperative communications","volume":"109","author":"Wang","year":"2019","journal-title":"Wirel. Pers. Commun."},{"key":"ref_137","first-page":"849","article-title":"Survey on Adaptive Channel Equalization Techniques using Particle Swarm Optimization","volume":"2","author":"Jaya","year":"2013","journal-title":"Int. J. Sci. Eng. Technol."},{"key":"ref_138","unstructured":"Acharya, U.K., and Kumar, S. (2019, January 13\u201315). Particle swarm optimization exponential constriction factor (PSO-ECF) based channel equalization. Proceedings of the 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/978-981-13-0341-8_4","article-title":"Quantum behaved particle swarm optimization technique applied to FIR-based linear and nonlinear channel equalizer","volume":"Volume 1","author":"Sinha","year":"2019","journal-title":"Advances in Computer Communication and Computational Sciences: Proceedings of IC4S 2017"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s00500-014-1262-4","article-title":"An adaptive particle swarm optimization method based on clustering","volume":"19","author":"Liang","year":"2015","journal-title":"Soft Comput."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"50388","DOI":"10.1109\/ACCESS.2019.2903137","article-title":"An improved hybrid method combining gravitational search algorithm with dynamic multi swarm particle swarm optimization","volume":"7","author":"Nagra","year":"2019","journal-title":"IEEE Access"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.neucom.2017.03.086","article-title":"Multi-objective dynamic economic emission dispatch using particle swarm optimisation variants","volume":"270","author":"Mason","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_143","first-page":"407","article-title":"A review of particle swarm optimization","volume":"99","author":"Jain","year":"2018","journal-title":"J. Inst. Eng."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.ins.2020.02.034","article-title":"Multipopulation cooperative particle swarm optimization with a mixed mutation strategy","volume":"529","author":"Li","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.egypro.2016.05.009","article-title":"Optimal photovoltaic placement by self-organizing hierarchical binary particle swarm optimization in distribution systems","volume":"89","author":"Phuangpornpitak","year":"2016","journal-title":"Energy Procedia"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.engappai.2015.06.013","article-title":"A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization","volume":"45","author":"Kodaz","year":"2015","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7710\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:22Z","timestamp":1760129182000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,6]]},"references-count":146,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187710"],"URL":"https:\/\/doi.org\/10.3390\/s23187710","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,6]]}}}