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    An atom search optimization approach for IIR system identification
    (Taylor & Francis Inc, 2023) Ekinci, Serdar; Budak, Cafer; Izci, Davut; Gider, Veysel
    Filtering, 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.
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    Boosted arithmetic optimization algorithm with elite opposition-based pattern search mechanism and its promise to design microstrip patch antenna for WLAN and WiMAX
    (Taylor and Francis Ltd., 2023) Özmen, Hüseyin; Ekinci, Serdar; İzci, Davut
    This paper investigates the performance of a novel artificial intelligence optimization technique in terms of designing a small size antenna that can be used for WLAN and WiMAX applications. In this regard, a boosted version of the arithmetic optimization algorithm is constructed as a novel artificial intelligence optimization technique with the aid of pattern search and elite opposition-based learning mechanisms. The proposed boosted arithmetic optimization algorithm is demonstrated for its superior explorative and exploitative behavior using classical fixed-dimensional, multimodal, and unimodal benchmark functions. The performance of the boosted arithmetic optimization algorithm is then presented for a real-world engineering optimization problem. For the latter challenge, a small size antenna that can be used for WLAN and WiMAX applications is designed. The obtained simulation results show that a compact small-sized patch antenna operating at WLAN and WiMAX frequencies can successfully be designed with the proposed boosted arithmetic optimization algorithm. Comparative evaluation against the state-of-the-art shows that efficiency is increased significantly since a bandwidth increase of up to (Formula presented.) is achieved even with a more than (Formula presented.) reduction in size.
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    Daily solar radiation prediction using LSTM neural networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Gider, Veysel; Budak, Cafer; İzci, Davut; Ekinci, Serdar
    The integration of solar energy with the smart grids and existing infrastructure makes it a cost-effective and environmentally-friendly solution to address the growing energy need. To make use of the potential of solar energy, several challenges such as the stability of generated energy and the supply-demand imbalance must be overcome. In this regard, an accurate forecast model for global solar radiation (GSR) can be useful for power generation planning and system reliability. The GSR estimate is regarded as the most significant and critical element in defining solar system characteristics, thus, it is crucial in predicting the generated energy. This work, therefore, employs long-short-term memory (LSTM) as a deep learning method to successfully estimate solar irradiance and capture the stochastic fluctuations. In this respect, the measurement data (from year 2021) obtained from the station installed in Dicle University (Turkey), Science and Technology Application and Research Centre (DUBTAM) were used, and the efficiency of the proposed method was evaluated. © 2022 IEEE.
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    PID controller design for DFIG-based wind turbine via reptile search algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2022) İzci, Davut; Ekinci, Serdar; Budak, Cafer; Gider, Veysel; 0000-0001-8359-0875
    This paper presents a new design procedure for a doubly fed induction generator (DFIG) based wind energy conversion (WEC) system in a wind turbine (WT) using a proportional-integral-derivative (PID) controller and a recent metaheuristic approach known as reptile search algorithm (RSA). As the control scheme has a significant role on the efficiency and reliability of DFIG-based WEC system, we aim to propose the RSA tuned PID controller as the most efficient approach to operate this system. To demonstrate the efficiency and reliability of the proposed design method, previously reported design schemes such as gravitational search algorithm, bacterial foraging optimization and particle swarm optimization based PID controller approaches were used for comparisons. The obtained results showed that the proposed reptile search algorithm tuned PID controller with 6th order transfer function model of doubly fed induction generator enhances the transient performance considerably compared to other reported design approaches for wind energy conversion system. © 2022 IEEE.