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Browsing by Author "Izci, D."

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    Article
    Augmented Hunger Games Search Algorithm Using Logarithmic Spiral Opposition-Based Learning for Function Optimization and Controller Design
    (King Saud University, 2024) Izci, D.; Ekinci, S.; Eker, E.; Kayri, M.
    This paper explains the construction of a novel augmented hunger games search algorithm using a logarithmic spiral opposition-based learning technique. The proposed algorithm (LsOBL-HGS) is used as an efficient tool for both function optimization and controller design. To assess the performance of the algorithm for function optimization, benchmark functions from the CEC2017 test suite were employed and comparisons were made with available and good performing algorithms. In terms of controller design, the proposed LsOBL-HGS algorithm was utilized to design a FOPID controlled magnetic ball suspension system. Comparative assessments were also performed for FOPID controller design, as well using other state-of-the-art methods reported for the magnetic ball suspension system. The results showed that the proposed LsOBL-HGS algorithm has good capability for FOPID controller design employed in a magnetic ball suspension system as it provided an improvement of more than 13% in terms of the transient response-related parameters and more than 34% in terms of bandwidth compared to the best-reported approach used for comparisons. © 2022 Karabuk University
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    Article
    A Novel Modified Opposition-Based Hunger Games Search Algorithm To Design Fractional Order Proportional-Integral Controller for Magnetic Ball Suspension System
    (John Wiley and Sons Inc, 2022) Izci, D.; Ekinci, S.; Eker, E.; Kayri, M.
    This study focuses on construction of novel enhanced metaheuristic algorithm using a modified opposition-based learning technique and the hunger games search algorithm. The proposed modified opposition-based hunger games search (mOBL-HGS) algorithm is aimed to be used as an efficient tool to tune a fractional order proportional-integral-derivative (FOPID) controller in order to control a magnetic ball suspension system with greater flexibility. The challenging benchmark functions from CEC2017 test suite are used to confirm the greater performance of the proposed mOBL-HGS algorithm. The proposed mOBL-HGS algorithm is also utilized to reach the optimum values of a FOPID controller employed in a magnetic ball suspension system in order to demonstrate its capability in terms of a complex real-world engineering problem. The latter case is confirmed through comparative evaluations of statistical analysis, convergence profile, transient response, frequency response, disturbance rejection, and robustness. The demonstrated results confirm the greater ability of the proposed approach to control a magnetic ball suspension system. © 2022 John Wiley & Sons Ltd.