Yazar "Akpamukcu, Mehmet" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    Combination of electromagnetic field and harris hawks optimization algorithms with optimization to optimization structure and its application for optimum power flow
    (Taylor & Francis Ltd, 2023) Akpamukcu, Mehmet; Ates, Abdullah; Akdag, Ozan
    Electromagnetic Field Optimization (EFO) and Harris Hawk Optimization (HHO) algorithms are combined with the optimization to optimization (OtoO) approach, and the EFO-HHO algorithm pair is presented in this study. EFO method was used as the essential algorithm and HHO method was used as the auxiliary algorithm according to the OtoO structure. The constant parameters (R_rate, Ps_rate, P_field, N_field) of the EFO algorithm that affect the optimization performance are optimized with the HHO optimization algorithm for the related optimization problem. The proposed method was tested on 10 different benchmark functions according to different dimensional (30, 50100). The EFO-HHO algorithm pair can produce better results than the existing literature, especially in cases of increased dimension with the proposed approach. In addition to these, the OPF problem was tested on the IEEE 30 test bus system for the engineering application of the proposed method. The results are compared with the existing literature results. As it can be seen from the results, it has been shown on the real engineering problem that the optimization performance can be increased with the OtoO approach without changing the basic philosophy of the EFO algorithm.
  • Küçük Resim Yok
    Öğe
    Modified monarch butterfly optimization with distribution functions and its application for 3 DOF Hover flight system
    (Springer London Ltd, 2022) Ates, Abdullah; Akpamukcu, Mehmet
    In this study, Modified Monarch Butterfly Optimization Algorithm ((MBO)-B-2) is proposed by modeling stochastic processes in Monarch Butterfly Optimization (MBO) algorithm with different random distribution functions. The proposed (MBO)-B-2 algorithm has been firstly tested with benchmark functions and the results have been compared with the literature and classical MBO algorithm. In order to analyze the performance of the proposed (MBO)-B-2 algorithm for the real engineering problem, the feedback gain matrix (K matrix) for the control of the 3 Degree of Freedom (3 DOF) Hover system has been optimized. The results have been compared with classical MBO, DSO (Discrete Stochastic Optimization), and SMDO (Stochastic Multi-parameter Divergence Optimization) optimization algorithms. The obtained results have been compared on 3 DOF Hover simulation models and real-time 3 DOF Hover experimental sets. The performance of the proposed (MBO)-B-2 algorithm in benchmark and real engineering problem tests has been shown theoretically and experimentally. Thus, it has been shown that the performance of algorithms can be increased without changing the basic philosophy of algorithms by modeling stochastic processes in algorithms with random distributions other than a uniform distribution. In addition to these, it has been determined that distribution function-based contributions can be applied to many of these algorithms.
  • Küçük Resim Yok
    Öğe
    Optimization to optimization (OtoO): optimize monarchy butterfly method with stochastics multi-parameter divergence method for benchmark functions and load frequency control
    (Springer, 2022) Ates, Abdullah; Akpamukcu, Mehmet
    Optimization to optimization (OtoO) approach is proposed in this study. It aims to increase an optimization algorithm performance. OtoO approach has two types of optimization methods. First is essential algorithm, which is used for solution of the basic problem. Second is auxiliary algorithm that adjusted the parameters of the essential algorithm. In this study, the monarchy butterfly optimization (MBO) method and stochastic multi-parameter divergence optimization (SMDO) method were defined as essential algorithm and auxiliary algorithm, respectively. Constant parameters of the MBO method that affect performance (Keep, Max. Step Size, period and BAR) are primarily optimized on benchmark functions with the SMDO algorithm, and results are compared with each other and classical MBO, ABC (Artificial Bee Colony), ACO (Ant Colony), BBO (Biogeography-based), SGA (Simple Genetic) and DE (Differential Evolution) algorithms. In addition, OtoO approach is also tried via composite benchmark functions. In addition, PI and PID controllers were designed for the load frequency control of a hybrid power system. Results are compared with the FA (Firefly Algorithm) and GA (Genetic Algorithm) results. Results demonstrate that the performance of algorithms can be increased without disrupting the basic philosophy of algorithms and hybridizing algorithms with the proposed OtoO approach via benchmark functions and engineering problems.