Yazar "Sag, Tahir" seçeneğine göre listele

Listeleniyor 1 - 7 / 7
  • Küçük Resim Yok
    Öğe
    Classification Rule Mining Approach Based on Multiobjective Optimization
    (IEEE, 2017) Sag, Tahir; Kahramanli, Humar
    In this paper, a novel approach for classification rule mining is presented. The remarkable relationship between the rule extraction procedure and the concept of multiobjective optimization is emphasized. The range values of features composing the rules are handled as decision variables in the modelled multiobjective optimization problem. The proposed method is applied to three well-known datasets in literature. These are Iris, Haberman's Survival Data and Pima Indians Diabetes Datasets obtained from machine learning repository of University of California at Irvine (UCI). The classification rules are extracted with 100% accuracy for all datasets. These experimental results are the best outcomes found in literature so far.
  • Küçük Resim Yok
    Öğe
    Color image segmentation based on multiobjective artificial bee colony optimization
    (ELSEVIER, 2015) Sag, Tahir; Cunkas, Mehmet
    This paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization. (C) 2015 Elsevier B.V. All rights reserved.
  • Küçük Resim Yok
    Öğe
    DETERMINATION OF INDUCTION MOTOR PARAMETERS BY DIFFERENTIAL EVOLUTION ALGORITHM AND GENETIC ALGORITHMS
    (AMER SOC MECHANICAL ENGINEERS, 2009) Cunkas, Mehmet; Sag, Tahir; Aslan, Mustafa
    In this paper, two algorithms, Differential Evolution Algorithm (DEA) and Genetic Algorithms (GAs), are applied to the offline identification of induction motor parameters. DEA is compared with the prediction errors and the genetic algorithm via determination parameters using nameplate data like starting torque, breakdown torque, and full-load torque in two different cases. Consequently, it is seen that DEA can be find more precise parameter values than the genetic algorithm and especially convergences to global optimum not to be stuck local optimum.
  • Küçük Resim Yok
    Öğe
    Determination of induction motor parameters with differential evolution algorithm
    (SPRINGER LONDON LTD, 2012) Arslan, Mustafa; Cunkas, Mehmet; Sag, Tahir
    In this study, the determination of equivalent circuit parameters of induction motors is carried out with differential evolution algorithm (DEA) and genetic algorithm (GA). As an objective function in the algorithms, the sum torque error at zero speed, pull-out, and rated speed is used. The determination of equivalent circuit parameters is performed with three induction motors of 2.2, 5.5, and 37 kW. In particular, the search ability of DEA is compared with GA by using the same population size, number of iteration, and crossover rate. In addition, the effects of the obtained equivalent circuit parameters on induction motors characteristics are investigated and presented with graphics. The results show that the use of DEA instead of GA increases the convergence sensitivity and reduces the simulation time.
  • Küçük Resim Yok
    Öğe
    Multiobjective genetic estimation to induction motor parameters
    (IEEE, 2007) Sag, Tahir; Cunkas, Mehmet
    In order to simplify the offline identification of induction motor parameters, a method based on optimization using a multiobjective genetic algorithm is proposed. The non-dominated sorting genetic algorithm (NSGA-II) is used to minimize the error between the actual data and an estimated model. The robustness of the method is shown by identifying parameters of the induction motor in three different cases. The simulation results show that the method successfully estimates the motor parameters.
  • Küçük Resim Yok
    Öğe
    A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: improved bee colony algorithm for multiobjective optimization)
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2016) Sag, Tahir; Cunkas, Mehmet
    This paper presents a new metaheuristic algorithm based on the artificial bee colony (ABC) algorithm for multiobjective optimization problems. The proposed hybrid algorithm, an improved bee colony algorithm for multiobjective optimization called IBMO, combines the main ideas of the simple ABC with nondominated sorting strategy corresponding to the principal framework of multiobjective optimization such as Pareto-dominance and crowding distance. A fixed-sized external archive to store the nondominated solutions and an improvement procedure to promote the convergence to true Pareto front are used. The presented approach, IBMO, is compared with four representatives of the state-of-the-art algorithms: NSGA2, SPEA2, OMOPSO, and AbYSS. IBMO and the selected algorithms from specialized literature are applied to several multiobjective benchmark functions by considering the number of function evaluations. Then four quality indicators are employed for performance evaluations: general distance, spread, maximum spread, and hypervolume. The results show that the IBMO is superior to the other methods.
  • Küçük Resim Yok
    Öğe
    A tool for multiobjective evolutionary algorithms
    (ELSEVIER SCI LTD, 2009) Sag, Tahir; Cunkas, Mehmet
    This paper introduces a software tool based on illustrative applications for the development, analysis and application of multiobjective evolutionary algorithms. The multiobjective evolutionary algorithms tool (MOEAT) written in C# using a variety of multiobjective evolutionary algorithms (MOEAs) offers a powerful environment for various kinds of optimization tasks. It has many useful features such as visualizing of the progress and the results of optimization in a dynamic or static mode, and decision variable settings. The performance measurements of well-known multiobjective evolutionary algorithms in MOEAT are done using benchmark problems. in addition, two case studies from engineering domain are presented. (C) 2009 Elsevier Ltd. All rights reserved.