Yazar "Hinislioglu, Yunus" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    Fuzzy Fitness Distance Balance Gradient-Based Optimization Algorithm (fFDBGBO): An Application to Design and Performance Optimization of PMSM
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Uzel, Hasan; Hinislioglu, Yunus; Dalcali, Adem; Guvenc, Ugur
    The exceptional properties of Permanent Magnet Synchronous Motors (PMSMs), including their small construction, high power-torque density, and high efficiency, make them one of the most popular electrical machines. However, the motor structure needs to be optimized for circumstances like boosting the PMSM's energy efficiency, optimizing output power, and lowering motor weight and cogging torque. This research aims to identify the optimal values for the parameters essential to achieving the most efficient design of a 15 kW PMSM. For this purpose, a new optimization algorithm is proposed. This proposed algorithm is an application of the fuzzy logic-based fitness distance balance design to the gradient-based optimization algorithm (GBO). The developed algorithm is called fuzzy Fitness Distance Balance Gradient-Based Optimization (fFDBGBO) algorithm. The proposed algorithm and various optimization algorithms with effective results in the literature were tested for the fitness function determined to find the optimal values of Embrace, Offset, Skew Width, Magnet Thickness, and slot bottom width (Bs1) of PMSM. The results indicate that the suggested algorithm achieves better performance than rival algorithms in terms of effectiveness. Compared to the GBO algorithm, the proposed fFDBGBO method achieves a 0.2979% increase in efficiency. Compared to other algorithms, the suggested algorithm's objective function has the highest slot fill factor (49.9995%), yet it still complies with the specified limit value. Relative to the initial design, the motor efficiency achieved with the fFDBGBO algorithm improved from 91.7788% to 93.0366%, while the magnet weight decreased from 2.14837 to 2.09475, and the total motor weight remained approximately the same as 53.2286 kg and 54.0436 kg.
  • Küçük Resim Yok
    Öğe
    A novel hyper-heuristic algorithm: an application to automatic voltage regulator
    (Springer Science and Business Media Deutschland GmbH, 2024) Hinislioglu, Yunus; Güvenç, Uǧur
    This paper presents a novel optimization algorithm called hyper-heuristic fitness-distance balance success-history-based adaptive differential evolution (HH-FDB-SHADE). The hyper-heuristic algorithms have two main structures: a hyper-selection framework and a low-level heuristic (LLH) pool. In the proposed algorithm, the FDB method is preferred as a high-level selection framework to evaluate the LLH pool algorithms. In addition, a total of 10 different strategies is derived from five mutation operators and two crossover methods for using them as the LLH pool. Balancing the exploration and exploitation capability of FDB is the main reason for being the selection framework of the proposed algorithm. The success of the HH-FDB-SHADE algorithm was tested on CEC-17 and CEC-20 benchmark test suits for different dimensional search spaces, and the obtained solutions from the HH-FDB-SHADE were compared to 10 different LLH pool algorithms. In addition, the HH-FDB-SHADE algorithm has been applied to optimize the control parameters of PID, PIDF, FOPID, and PIDD2 in the optimal automatic voltage regulator (AVR) design problem to reveal the improved algorithm's performance more clearly and prove its success in solving engineering problems. The results obtained from the AVR system are compared with five other effective meta-heuristic search algorithms such as the fitness-distance balance Lévy Flight distribution, differential evolution, Harris–Hawks optimization, Barnacles mating optimizer, and Moth–Flame optimization algorithms in the literature. The results of the statistical analyses indicate that HH-FDB-SHADE is the best-ranked algorithm for solving CEC-17 and CEC-20 benchmark problems and gives better results compared to the LLH pool algorithms. Besides, the proposed algorithm is more effective and robust than five other meta-heuristic algorithms in solving optimal AVR design problems. © 2024 Elsevier B.V., All rights reserved.