An atom search optimization approach for IIR system identification

ABSTRACTFiltering, or digital signal processing, is a significant and fundamental requirement in fields such as signal systems and computers. The process of designing optimal digital filters is difficult, which has led researchers to design filters using emerging evolutionary computations. Metaheuristics have emerged as the most promising tool for solving optimization problems, with excellent development and improvement. However, it has not been clear how to select the best performing metaheuristic to design an optimal digital filter. In this paper, a digital infinite impulse response (IIR) filter is constructed using the atom search optimization (ASO) algorithm impressed by the physical motion of atoms in nature based on molecular dynamics. The simulation results obtained are extensively compared with the results of other optimization algorithms such as moth flame optimization, gravitational search algorithm and artificial bee colony optimization. ASO was found to have the highest percentage of improvement. Furthermore, eight cases are analyzed across four numerical filter instances with the same degree and four with reduced degree, and the results are validated by outperforming several different algorithm-based approaches in the literature. The stability analysis on the basis of pole zero diagrams further cements the efficacy of the ASO for IIR system identification problem.KEYWORDS: Digital IIR filtersatom search optimization algorithmmetaheuristic algorithm Disclosure statementThe authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.Data availability statementData sharing is not applicable to this article as no datasets were generated or analyzed during the current study.Additional informationFundingNo funding has been received for this work.Notes on contributorsSerdar EkinciSerdar Ekinci received his BSc degree in Control Engineering, and his MSc and PhD degrees in Electrical Engineering all from Istanbul Technical University (ITU), in 2007, 2010 and 2015, respectively. He is currently an Associate Professor working in Department of Computer Engineering at Batman University, Turkey. His areas of interest are electrical power systems, stability, control technology and the applications of metaheuristic optimization algorithms to various control systems.Cafer BudakCafer Budak received his BSc and MSc degrees from the Department of Electrical and Electronics Engineering at Dicle University. He earned his PhD degree from the Department of Electrical and Electronics Engineering at Fırat University. Currently serving as an Associate Professor in the Department of Electrical and Electronics Engineering at Dicle University. He is actively involved in research and work related to artificial intelligence, machine learning, deep learning, image processing, drug discovery, control system design, and optimization.Davut IzciDavut Izci received his BSc degree from Dicle University, Turkey, in Electrical and Electronic Engineering and his MSc and PhD degrees from Newcastle University, England – UK, in Mechatronics and Microsystems, respectively. He is currently an Associate Professor working on optimization, control system design, sensing applications, energy harvesting, microsystems development and applications of metaheuristic optimization techniques to different control systems and real-world engineering problems.Veysel GiderVeysel Gider is currently pursuing his PhD studies in the Department of Electrical and Electronics Engineering at Dicle University, following his BSc and MSc degrees from the Department of Electrical and Electronics Engineering at Batman University. Additionally, he serves as an instructor at the Distance Education Application and Research Center of Batman University. His research focuses on applications of artificial intelligence, machine learning, graphic neural networks, drug discovery, drug analysis, control systems, and optimization techniques.