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Öğe Color image segmentation based on multiobjective artificial bee colony optimization(ELSEVIER, 2015) Sag, Tahir; Cunkas, MehmetThis 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.Öğe A Comparative Study of Artificial Neural Network and ANFIS for Short Term Load Forecasting(IEEE, 2014) Cevik, Hasan Huseyin; Cunkas, MehmetShort term load forecast provides market participants the opportunity to balance their generation and/or consumption needs and contractual obligation one day in advance. It also helps to determine reference price for electricity energy and provide system operator a balanced system. This paper presents a comparative study of ANFIS and ANN methods for short term load forecast. Using the load, season and temperature data of Turkey between years of 2009-2011, the prediction is carried out for 2012. The mean absolute percentage errors for ANFIS and ANN models are found as 1.85 and 2.02, respectively in all days except holidays of 2012.Öğe A COMPARATIVE STUDY ON PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHMS FOR TRAVELING SALESMAN PROBLEMS(TAYLOR & FRANCIS INC, 2009) Cunkas, Mehmet; Ozsaglam, M. YasinThis article deals with a performance evaluation of particle swarm optimization (PSO) and genetic algorithms (GA) for traveling salesman problem (TSP). This problem is known to be NP-hard, and consists of the solution containing N! permutations. The objective of the study is to compare the ability to solve the large-scale and other benchmark problems for both algorithms. All simulation has been performed using a software program developed in the Delphi environment. As yet, overall results show that genetic algorithms generally can find better solutions compared to the PSO algorithm, but in terms of average generation it is not good enough.Öğe Cost optimization of feed mixes by genetic algorithms(ELSEVIER SCI LTD, 2009) Sahman, M. Akif; Cunkas, Mehmet; Inal, Seref; Inal, Fatma; Coskun, Behic; Taskiran, UgurThe cost optimization is a key element to determine the least-cost feed mixture according to animals' nutrient requirements and the effective use of the sources. In this paper, the cost optimization of feeds is performed by genetic algorithm, considering the growing style and type, age, nutritional requirement and feedstuff costs for poultry and different types of animals. The proposed method is compared with linear programming approach to measure its performance. The obtained results show that Genetic algorithms could be applicable to the cost optimization of the feed mixtures. In addition, a software program is developed by using Delphi environment. which provides flexible, extensible and user-friendly framework for tuning the heuristic relevant parameters and improving the solution quality. (C) 2009 Elsevier Ltd. All rights reserved.Öğe Cost optimization of submersible motors using a genetic algorithm and a finite element method(SPRINGER LONDON LTD, 2007) Cunkas, Mehmet; Akkaya, Ramazan; Bilgin, OsmanThis paper presents an optimal design method to optimize cost of three-phase submersible motors. The optimally designed motor is compared with an industrial motor having the same ratings. The motor design procedure consists of a system of non-linear equations, which imposes induction motor characteristics, motor performance, magnetic stresses, and thermal limits. The genetic algorithm (GA) is used for cost optimization, and a software algorithm has been developed. As a result of the realized optimization, besides the improvements on the motor cost, motor torque improvements have also been acquired. The 2-D finite element method (FEM) is then used to confirm the validity of the optimal design. Computer simulation results are given to show the effectiveness of the proposed design process that can achieve a good prediction of the motor performance. Through the studies accomplished, it has been observed that submersible induction motors' torques and efficiencies improve, their length reduces, and hence some materialÖğe Day Ahead Wind Power Forecasting Using Complex Valued Neural Network(IEEE, 2018) Cevik, Hasan Huseyin; Acar, Yunus Emre; Cunkas, MehmetWind power forecast is one of the daily processes performed by Wind Power Plants (WPPs). It is very important to provide the generation-consumption balance one-day in advance for electric power system. In this study a day ahead wind power forecast in hourly bases is carried out for seven WPPs. The data used in this forecast is composed of the generation data of seven WPPs and the numerical weather forecasts of these WPP site. While the train data consist of 12-month data, the test data consist of 6-month data. Complex Valued Neural Network (CVNN), a special kind of artificial neural network (ANN), are preferred as the forecast method and compared with Real Valued Neural Network (RVNN). While hour, wind speed forecasts and wind direction forecasts are used as the system inputs, the output is forecasted wind power. Since the CVNN works with complex number, the non-complex inputs are converted to complex values. Normalized Mean Absolute Error (NMAE) and Normalized Root Mean Square Error (NRMSE) are preferred to show the forecast accuracy. While RVNN has an average of 12.82% NMAE and 16.8% NRMS, CVNN has 11.75% NMAE and 15.77% NRMSE. It is seen that CVNN method is more successful with the lower error rates than RVNN. Therefore, CVNN can be used as an effective tool for wind power forecast.Öğe DESIGN OPTIMIZATION OF SUBMERSIBLE INDUCTION MOTORS by MULTIOBJECTIVE FUZZY GENETIC ALGORITHM(GAZI UNIV, FAC ENGINEERING ARCHITECTURE, 2008) Cunkas, Mehmet; Urkmez, AbdullahThis paper presents multiobjective fuzzy genetic algorithm optimization approach to a submersible motor design. Utilizing the concept of fuzzy sets and convex fuzzy decision making, the motor design task is formulated as a multiobjective fuzzy optimization problem and solved using a genetic algorithm. The two-dimensional Finite Element Method (FEM) is then used to confirm the validity of the optimal design. The optimization results show the effectiveness and achievement of the proposed method.Öğe DETERMINATION OF INDUCTION MOTOR PARAMETERS BY DIFFERENTIAL EVOLUTION ALGORITHM AND GENETIC ALGORITHMS(AMER SOC MECHANICAL ENGINEERS, 2009) Cunkas, Mehmet; Sag, Tahir; Aslan, MustafaIn 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.Öğe Determination of induction motor parameters with differential evolution algorithm(SPRINGER LONDON LTD, 2012) Arslan, Mustafa; Cunkas, Mehmet; Sag, TahirIn 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.Öğe Forecasting Hourly Electricity Demand Using a Hybrid Method(IEEE, 2017) Cevik, Hasan Huseyin; Harmanci, Huseyin; Cunkas, MehmetIn the electricity sector, new sides have emerged with the development of technology and the increasing the electric energy need. Today, electricity has become a product that is bought and sold in the market environment. Forecasting which is the first step of plans and planning have become much more important and have been made mandatory for the market participants by energy market regulators. In this study, a short-term electricity load forecast is done for 24 hours of next day. Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) techniques are used for the forecast method in a hybrid form. The weights of ANN is updated by PSO in learning phase. Historical load consumption data, historical daily mean air temperature data and season are selected as inputs. Load data of 4 years on hourly basis are taken into account. Train and test data are considered as 3 years and 1 year, respectively. The MAPE error is found as 2.15 for one year period on an hourly basis.Öğe Fuzzy logic-based induction motor protection system(SPRINGER, 2013) Uyar, Okan; Cunkas, MehmetThe protection is very important to detect abnormal motor running conditions such as over current, over voltage, overload, over temperature, and so on. When a failure is sensed by the protection system, a time delay should be specified to trip the motor. In the classical systems, motors are stopped with the time delay, which is adjusted constantly without considering the fault level. This paper presents a fuzzy logic-based protection system covering six different fault parameters for induction motors. This paper focuses on a new time-delay calculation for stopping induction motor and improves the overall detection performance. The time delay is computed by fuzzy logic method according to various fault parameters when one of the failures occurs on the motor. This system is successfully tested in real-time faults on the motor, and it shows that it provides sensitive protection by fuzzy rules.Öğe Genetic Algorithms for Mesh Surface Smoothing(SPIE-INT SOC OPTICAL ENGINEERING, 2015) Ozsaglam, Mehmet Yasin; Cunkas, MehmetThis paper presents a new 3D mesh smoothing algorithm which is based on evolutionary methods. This method is a new optimization technique with Genetic algorithm. The main approach is based on expanding the search space by generating new meshes as genetic individuals. Features and shrinkage of models are preserved that are the main problems of existing smoothing algorithms. So with this method, over-smoothing effects are reduced and undesirable noises are effectively removed.Öğe Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method(PERGAMON-ELSEVIER SCIENCE LTD, 2011) Asilturk, Ilhan; Cunkas, MehmetMachine parts during their useful life are significantly influenced by surface roughness quality. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this study, the surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental design is implemented to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. Multiple regression and neural network-based models are compared using statistical methods. It is clearly seen that the proposed models are capable of prediction of the surface roughness. The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model. (C) 2010 Elsevier Ltd. All rights reserved.Öğe Multiobjective genetic estimation to induction motor parameters(IEEE, 2007) Sag, Tahir; Cunkas, MehmetIn 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.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, MehmetThis 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.Öğe A new multistage short-term wind power forecast model using decomposition and artificial intelligence methods(ELSEVIER, 2019) Cevik, Hasan Huseyin; Cunkas, Mehmet; Polat, KemalIn this study, a new forecast model consist of three stages is proposed for the next hour wind power. In the first stage, wind speed, wind direction, and wind power have been forecasted by using historical data. Artificial Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Support Vector Regression (SVR) have been chosen as forecast methods, while Empirical Mode Decomposition (EMD) and Stationary Wavelet Decomposition (SWD) methods have been preferred as pre-processing methods. The other two stages have been used to improve the wind power forecast value obtained at the end of the first stage. In the second stage, the forecast values found in the first stage have been applied to the same forecast methods, and wind power forecast value has been updated. In the third stage, a correction process is applied, and the final forecast value is obtained. While four-year data are selected as train data, two-year data are tested. SWD-ANFIS has given the best results in the first stage while ANN has given the best result in the second stage. Finally, the ensemble result has been found by taking the weighted average of the results of the three methods. Mean Absolute Error (MAE) values found at each stage are the 0.333, 0.294 and 0.278, respectively. The obtained results have been compared with literature studies. The results show that the proposed multistage forecast model is capable of wind power forecasting efficiently and produce very close values to the actual data. (C) 2019 Elsevier B.V. All rights reserved.Öğe Parameter determination of induction machines by hybrid genetic algorithms(SPRINGER-VERLAG BERLIN, 2007) Mutluer, Muemtaz; Bilgin, Osman; Cunkas, MehmetIn general, a genetic algorithm combined with other algorithms (e.g. tabu search, simulated annealing, etc.) is well known to be a powerful approach. In this paper, an efficient hybrid approach containing local search and genetic algorithms is presented. The purpose of the using local search mechanisms is to provide better the solution quality and to increase the convergence speed. It is demonstrated that the performance of the proposed algorithms is significantly better than the conventional genetic algorithm methods.Öğe A Review on Optimization of Electrical Machines Using Evolutionary Algorithms(Selçuk Üniversitesi, 2005) Cunkas, MehmetThe majority of the electricity power used in industry is consumed by electrical machines. This situation shows the importance of the optimization approaches. This paper presents a literature review about the optimization methods for electric machinery. Evolution algorithms have been used for a long time in the design optimization of electrical machines. In this paper, it is aimed to review the current methods and applications, taking into the up-to-date papersÖğe Short-term load forecasting using fuzzy logic and ANFIS(SPRINGER LONDON LTD, 2015) Cevik, Hasan Huseyin; Cunkas, MehmetThis paper presents short-term load forecasting models, which are developed by using fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS). Firstly, historical data are analyzed and weekdays are grouped according to their load characteristics. Then, historical load, temperature difference and season are selected as inputs. In general literature, fuzzy logic hourly load forecasts are tested in the range a few days or a few weeks. Unlike previous studies, the hourly load forecast is carried out for 1 year. This paper shows that fuzzy logic can give good results in very large test data sets for 1 year. Besides, for countries with large areas, the temperature data taken from only one point would lead to increase the forecasting errors. Therefore, the average of temperature for six cities having the maximum power consumption is weighted average. The mean absolute percentage errors of the fuzzy logic and ANFIS models in terms of prediction accuracy are obtained as 2.1 and 1.85, respectively. The results show that the proposed fuzzy logic and ANFIS models are capable of load forecasting efficiently and produce very close values to the actual data and are the alternative way for short-term load forecasting in Turkey.Öğe A tool for multiobjective evolutionary algorithms(ELSEVIER SCI LTD, 2009) Sag, Tahir; Cunkas, MehmetThis 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.
