Computer Engineering Commons^™

A Problem Approximation Surrogate Model (Pasm) For Fitness Approximation In Optimizing The Quantization Table For The Jpeg Baseline Algorithm, Vinoth Kumar Balasubramanian, Karpagam Manavalan

Turkish Journal of Electrical Engineering and Computer Sciences

The quantization table in the baseline Joint Photographic Experts Group (JPEG) algorithm plays an important role in compression/quality trade-off. Hence the detection of the optimal quantization table is viewed as an optimization problem. The genetic algorithm (GA) is an attractive optimization tool by many researchers for this application due to its ability in dealing with complex problems. In spite of its advantages, the GA requires more computation time to achieve an optimal solution if it has an expensive fitness evaluation. This paper proposes a problem approximation surrogate model (PASM) for fitness approximation to assist the GA in optimizing the quantization …

Applying Metaheuristic Optimization Methods To Design Novel Adaptive Pi-Type Fuzzy Logic Controllers For Load-Frequency Control In A Large-Scale Power Grid, Thimaiphuong Dao, Yaonan Wang, Ngockhoat Nguyen

Turkish Journal of Electrical Engineering and Computer Sciences

Due to the complexity and diversity of large-scale power systems in practice, designing load-frequency control (LFC) strategies against load variations faces big challenges to ensure the stability and economy of the network. The focus of this paper is to design a novel adaptive PI-type fuzzy logic (FL)-based LFC architecture for solving the LFC problem in such an interconnected electric power grid. Applying 2 biologically inspired optimization methods, namely particle swarm optimization method and a genetic algorithm, the membership functions and rule base of a basic PI-type FL model were parameterized and optimized simultaneously and successfully. An online self-tuning method was …

The Parallel Resonance Impedance Detection Method For Parameter Estimation Of Power Line And Transformer By Using Csa, Ga, And Pso, Bahadir Akbal, Abdullah Ürkmez

Turkish Journal of Electrical Engineering and Computer Sciences

Power line parameters are an important factor in relay applications and power quality studies. In the literature, the phasor measurement unit method and measuring of current and voltage at two ends of the power line were usually used to estimate the power line parameters. In this study, the parallel resonance impedance detection method was used to estimate the power line parameter to obtain input data. The real measurement values are used to obtain parallel resonance impedance in this method. The real measurement values include the measurement errors of the current and voltage transformer. Thus, the estimated parameter values are realistic. …

Papr Reduction Using Genetic Algorithm In Lifting-Based Wavelet Packet Modulation Systems, Necmi̇ Taşpinar, Yüksel Tokur Bozkurt

Turkish Journal of Electrical Engineering and Computer Sciences

Wavelet packet modulation (WPM) is a potential candidate in wireless communication systems by virtue of its flexibility and modular implementation capability. However, WPM suffers from high peak-to-average power ratio (PAPR), which results in signal distortion when a high-power amplifier is employed at the transmitter. The partial transmit sequence (PTS) is an attractive PAPR reduction method, but its computational complexity is high. In this paper, we propose a PTS based on the genetic algorithm (GA) scheme (GA-PTS) to reduce the computational complexity of the PTS in the lifting-based WPM (LBWPM) systems. Simulation results show that the proposed GA-PTS scheme provides significant …

Speciation-Based Genetic Algorithm In Analog Circuit Design, Hasari̇ Karci̇, Gülay Tohumoğlu, Ari̇f Nacaroğlu

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a speciation procedure that improves the local search capability of the genetic algorithm in analog circuit design. There is no need for additional circuit simulation in order to apply this procedure. The procedure is tested in Gaussian, sigmoid, cube, and square circuit design problems. Two sets of 125 simulations with the same seed values are performed for each problem using both the proposed procedure and the canonical genetic algorithm. The simulation results show that the method is statistically better than the canonical genetic algorithm, which suffers from bad locality. The effects of the population size and speciation …

Optimization With Genetic Algorithm Of Temperature-Dependent Fiber Length Of L-Band Edfa Gain, Murat Yücel, Damt Adnan Mustafa Mustafa

Turkish Journal of Electrical Engineering and Computer Sciences

Erbium-doped fiber amplifiers (EDFAs) have great importance in long-distance communication. It is required to have equal gain for all signals that are transferred and to avoid loss in the receiver of long-distance communication systems. However, temperature dependence changes the output spectrum of the designed gain-flattening systems. In this study, each erbium-doped fiber (EDF) length of a two-stage L-band EDFA has been optimized using a genetic algorithm method; because of the temperature dependence of EDFAs, there is no general rule. Thus, a simple, fast, dynamic, and highly accurate model has been developed and obtained for different EDF lengths that will fix …

An Intelligent Design Optimization Of A Permanent Magnet Synchronous Motor By Artificial Bee Colony Algorithm, Mümtaz Mutluer, Osman Bi̇lgi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

The artificial bee colony algorithm is one of the latest stochastic methods based on swarm intelligence. The algorithm simulates the foraging behavior of honeybees. The structure of the algorithm is quite simple and its coding is very easy. This paper proposes a design optimization based on geometrical variables to obtain a highly efficient surface-mounted permanent magnet synchronous motor with concentrated winding by use of the artificial bee colony algorithm. Input parameters for the algorithm are the geometrical variables of the motor. This approach is more advantageous than finite element analysis requiring a long period of time. Results of the artificial …

Design And Implementation Of A Genetic Algorithm Ip Core On An Fpga For Path Planning Of Mobile Robots, Adem Tuncer, Mehmet Yildirim

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a hardware realization of a genetic algorithm (GA) for the path planning problem of mobile robots on a field programmable gate array (FPGA). A customized GA intellectual property (IP) core was designed and implemented on an FPGA. A Xilinx xupv5-lx110t FPGA device was used as the hardware platform. The proposed GA IP core was applied to a Pioneer 3-DX mobile robot to confirm its path planning performance. For localization tasks, a camera mounted on the ceiling of the laboratory was utilized to receive images and allow the robot to determine its own location and the obstacles in …

An Elegant Emergence Of Optimal Siting And Sizing Of Multiple Distributed Generators Used For Transmission Congestion Relief, Rajamanickam Manickaraj Sasiraja, Velu Suresh Kumar, Sankaralingam Ponmani

Turkish Journal of Electrical Engineering and Computer Sciences

After the implementation of deregulation in a power system, an appreciable volume of renewable energy sources is used to generate electric power. Even though they are intended to improve the reliability of the power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand, and failures of other equipment lead to congestion in one or more transmission lines. There are several ways to alleviate this transmission congestion, such as the installation of new generation facilities in the place where the demand is high, the addition of a new transmission facility, generation rescheduling, and curtailment of load …

Bull Optimization Algorithm Based On Genetic Operators For Continuous Optimization Problems, Oğuz Findik

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, the researcher proposes a new evolutionary optimization algorithm that depends on genetic operators such as crossover and mutation, referred to as the bull optimization algorithm (BOA). This new optimization algorithm is called the BOA because the best individual is used to produce offspring individuals. The selection algorithm used in the genetic algorithm (GA) is removed from the proposed algorithm. Instead of the selection algorithm, individuals initially produced attempt to achieve better individuals. In the proposed method, crossover operation is always performed by using the best individual. The mutation process is carried out by using individual positions. In …

Ga Based Adaptive Receiver For Mc-Cdma System, Muhammad Adnan Khan, Muhammad Umair, Muhammad Aamer Saleem Choudhry

Turkish Journal of Electrical Engineering and Computer Sciences

Multicarrier systems like the multicarrier code division multiple access (MC-CDMA) systems are designed for maximum usability of available bandwidth. We use the MC-CDMA system with Alamouti's space time coding in this paper. We propose the genetic algorithm (GA) in order to calculate MC-CDMA receiver weights with two variation schemes. The proposed schemes reduce receiver complexity. The bit error rate and convergence rate are also improved by increasing the number of genes and chromosomes of the GA in both schemes as compared with conventional LMS based receivers of the MC-CDMA system. This is verified via simulations.

Privacy Preserving In Association Rules Using A Genetic Algorithm, Rahat Ali Shah, Sohail Asghar

Turkish Journal of Electrical Engineering and Computer Sciences

Association rule mining is one of the data mining techniques used to extract hidden knowledge from large datasets. This hidden knowledge contains useful and confidential information that users want to keep private from the public. Similarly, privacy preserving data mining techniques are used to preserve such confidential information or restrictive patterns from unauthorized access. The pattern can be represented in the form of a frequent itemset or association rule. Furthermore, a rule or pattern is marked as sensitive if its disclosure risk is above a given threshold. Numerous techniques have been used to hide sensitive association rules by performing some …

Optimal Reactive Power Flow Solution In Multiterminal Ac-Dc Systems Based On Artificial Bee Colony Algorithm, Faruk Yalçin, Uğur Ari̇foğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Active power loss optimization is one of the important goals in electrical power systems and it is provided by optimal reactive power flow (ORPF). In this study, a new approach for the solution of the ORPF in multiterminal AC-DC systems is proposed. This approach provides 3 main contributions. First, the convergence problem in the AC-DC power flow is solved. Second, the problem of getting stuck in local minima during the optimization process is overcome. Third, a better global optimum point is obtained for the ORPF. Active power loss optimization is implemented through the artificial bee colony (ABC) algorithm, which is …

Analysis Of A Rule-Based Curriculum Plan Optimization System With Spearman Rank Correlation, Di̇dem Abi̇di̇n, Hafi̇ze Şen Çakir

Turkish Journal of Electrical Engineering and Computer Sciences

In corporations, accurate planning should be applied to manage the in-service training task within an optimum time period and without hindering the working tempo of the employees. For this reason, it is better to consider the curriculum planning task as a timetabling problem. However, when the timetables are prepared manually, it may turn out to be a complicated and time-consuming problem. In this study, it is aimed to evaluate the results of software introduced previously, which seeks to find a solution to the curriculum planning problem of in-service training programs in corporations using a rule-based genetic algorithm (GA). The input …

Solving A New Bi-Objective Joint Replenishment Inventory Model With Modified Rand And Genetic Algorithms, Ommolbanin Yousefi, Seyed Jafar Sadjadi

Turkish Journal of Electrical Engineering and Computer Sciences

There are many cases in real inventory systems where more than one objective must be optimized. The main purpose of this research is to develop a multiobjective joint replenishment problem (JRP), where one objective is the minimization of the total inventory investment and another is the minimization of the total inventory ordering and holding costs. To solve the suggested model, 3 algorithms are proposed. In the first algorithm, the existing RAND method, called the best heuristic for solving the JRP, is modified and a new heuristic algorithm is developed to be applicable to the JRP with 2 objectives. The second …

Control Of Svc Based On The Sliding Mode Control Method, Ercan Köse, Hakan Kizmaz, Kadi̇r Abaci, Saadetti̇n Aksoy

Turkish Journal of Electrical Engineering and Computer Sciences

A genetic algorithm (GA)-based sliding mode controller is proposed to improve the voltage stability of a power system with a static var compensator. The proposed controller is examined for improving the load bus voltage, which changes under different demanding powers, and its performance for transient analysis is compared with the Ziegler--Nichols proportional-integral (ZNPI), Lyapunov-based sliding mode control (LASMC), and GA-based proportional-integral-derivative (GAPID) controllers. The dynamic equations, consisting of a 2-bus nonlinear system, are converted to a mathematical description of sliding mode techniques. The optimum values of the sliding mode controller and proportional-integral-derivative (PID) coefficients that are required are calculated using …

Induction Motor Parameter Estimation Using Metaheuristic Methods, Ali̇ İhsan Çanakoğlu, Asim Gökhan Yetgi̇n, Hasan Temurtaş, Mustafa Turan

Turkish Journal of Electrical Engineering and Computer Sciences

The steady-state equivalent circuit parameters of an induction motor can be estimated using the operation characteristics that are provided by manufacturers. The characteristics of the motor used in estimation methods are the starting, maximum, and nominal torque values; the power factor; and efficiency. The operation characteristics of a motor given in data sheets are generally based on design parameters and are not suitable with real values. For this reason, in this paper, the data used in the parameter estimation for induction motors are taken from the literature. Using an optimization method for parameter estimation is useful for comparing the manufacturer …

Symbol Detection Using The Differential Evolution Algorithm In Mimo-Ofdm Systems, Muhammet Nuri̇ Seyman, Necmi̇ Taşpinar

Turkish Journal of Electrical Engineering and Computer Sciences

Channel estimation and symbol detection in multiple-input and multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems are essential tasks. Although the maximum likelihood (ML) detector reveals excellent performance for symbol detection, the computational complexity of this algorithm is extremely high in systems with more transmitter antennas and high-order constellation size. In this paper, we propose the differential evolution (DE) algorithm in order to reduce the search space of the ML detector and the computational complexity of symbol detection in MIMO-OFDM systems. The DE algorithm is also compared to some heuristic approaches, such as the genetic algorithm and particle swarm optimization. According …

Bidding Strategy Of Generation Companies In A Competitive Electricity Market Using The Shuffled Frog Leaping Algorithm, Vijaya Kumar Jonnalagadda, Vinod Kumar Dulla Mallesham

Turkish Journal of Electrical Engineering and Computer Sciences

In a competitive electricity market, generation companies need suitable bidding models to maximize their profit. Therefore, each supplier will bid strategically for choosing the bidding coefficients to counter the competitors' bidding strategies. In this paper, the optimal bidding strategy problem is solved using a novel algorithm based on the shuffled frog leaping algorithm (SFLA). It is a memetic metaheuristic that is designed to seek a global optimal solution by performing a heuristic search. It combines the benefits of the genetic-based memetic algorithm (MA) and the social behavior-based particle swarm optimization (PSO). This allows it to have a more precise search …

A New Method Based On Sensitivity Analysis To Optimize The Placement Of Ssscs, Hamed Hashemi Dezaki, Masoud Mohammadalizadeh Shabestary, Hossein Askarian Abyaneh, Gevorg Babamalek Gharehpetian, Mehdi Garmrudi

Turkish Journal of Electrical Engineering and Computer Sciences

The flexible altering current transmission system (FACTS) controllers such as static synchronous series compensators (SSSCs) and unified power flow controllers can strongly improve the different parameters of power systems. They can be used to improve the transient stabilities, voltage profiles, lines transmission capabilities, etc. Therefore, the optimized allocation of FACTS devices is an important issue in recent research. The optimal placements of FACTS devices can have a determinant effect on the performance of a system, too. In this paper, the proper allocation of SSSCs is proposed. Sensitivity analysis is used in order to model SSSCs' effects on the power system. …

Optimized Operation And Maintenance Costs To Improve System Reliability By Decreasing The Failure Rate Of Distribution Lines, Hamed Hashemi Dezaki, Seyed Hossein Hosseinian, Hossein Askarian Abyaneh, Seyed Mohammad Mousavi Agah

Turkish Journal of Electrical Engineering and Computer Sciences

Improving distribution system reliability has received a great deal of attention in recent years. Because of the limitation in expected budgets, it is desirable to determine the most efficient strategy to improve system reliability. This paper proposes a novel method to determine the optimized operation and maintenance costs in order to decrease the failure of system components. The proposed objective function includes the average system frequency interruption index (ASIFI) value. To achieve the best strategy to decrease failures of system components, it is necessary to find the minimum value of the objective function, considering the constraints of operation and maintenance …

Optimal Sizing And Siting Distributed Generation Resources Using A Multiobjective Algorithm, Seyed Amir Hosseini, Seyed Siavash Karimi Madahi, Farzad Razavi, Mohsen Karami, Ali Asghar Ghadimi

Turkish Journal of Electrical Engineering and Computer Sciences

The restructuring of the electrical market, improvement in the technologies of energy production, and energy crisis have paved the way for increasing applications of distributed generation (DG) resources in recent years. Installing DG units in a distribution network may result in positive impacts, such as voltage profile improvement and loss reduction, and negative impacts, such as an increase in the short-circuit level. These impacts depend on the type, capacity, and place of these resources. Therefore, finding the optimal place and capacity of DG resources is of crucial importance. Accordingly, this paper is aimed at finding the optimal place and capacity …

Solution To The Unit Commitment Problem Using An Artificial Neural Network, Mehdi Zareian Jahromi, Mohammad Mehdi Hosseini Bioki, Masoud Rashidi Nejad, Roohollah Fadaeinedjad

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes a real-time solution to the unit commitment problem by considering different constraints like ramp-up rate, unit operation emissions, next hours load, and minimum down time. In this method, an optimized trade-off between cost and emission has been taken into consideration. The effectiveness of the proposed method was verified by the significant outcomes demonstrated.

Hybrid Of Genetic Algorithm And Great Deluge Algorithm For Rough Set Attribute Reduction, Najmeh Sadat Jaddi, Salwani Abdullah

Turkish Journal of Electrical Engineering and Computer Sciences

The attribute reduction problem is the process of reducing unimportant attributes from a decision system to decrease the difficulty of data mining or knowledge discovery tasks. Many algorithms have been used to optimize this problem in rough set theory. The genetic algorithm (GA) is one of the algorithms that has already been applied to optimize this problem. This paper proposes 2 kinds of memetic algorithms, which are a hybridization of the GA, with 2 versions (linear and nonlinear) of the great deluge (GD) algorithm. The purpose of this hybridization is to investigate the ability of this local search algorithm to …

Optimized Features Selection For Gender Classification Using Optimization Algorithms, Sajid Ali Khan, Muhammad Nazir, Naveed Riaz

Turkish Journal of Electrical Engineering and Computer Sciences

Optimized feature selection is an important task in gender classification. The optimized features not only reduce the dimensions, but also reduce the error rate. In this paper, we have proposed a technique for the extraction of facial features using both appearance-based and geometric-based feature extraction methods. The extracted features are then optimized using particle swarm optimization (PSO) and the bee algorithm. The geometric-based features are optimized by PSO with ensemble classifier optimization by the genetic algorithm. Using this approach, we have obtained promising results in terms of the classification error rate and computation time minimization. Moreover, our optimized feature-based method …

Short-Term Nodal Congestion Price Forecasting In A Large-Scale Power Market Using Ann With Genetic Optimization Training, Majid Moazzami, Rahmat Allah Hooshmand

Turkish Journal of Electrical Engineering and Computer Sciences

In a daily power market, price and load forecasting are the most important signals for the market participants. In this paper, an accurate feed-forward neural network model with a genetic optimization Levenberg-Marquardt back propagation training algorithm is employed for short-term nodal congestion price forecasting in different zones of a large-scale power market. The use of genetic algorithms for neural network training optimization has a remarkable effect on the accuracy of price forecasting in a large-scale power market. The necessary data for neural network training are obtained by solving optimal power flow equations that take into account all effective constraints at …

Using Learning Automata For Multi-Objective Generation Dispatch Considering Cost, Voltage Stability And Power Losses, Ari̇f Karakaş, Celal Kocatepe, Fangxing Li

Turkish Journal of Electrical Engineering and Computer Sciences

The economical and secure operation of power systems has significant importance. Due to technical limitations, the best economical operation point is not always the desired operating point for system stability or power losses. In this study, first, the most economical operating point is obtained by solving the non-linear, network-constrained economic dispatch problem using a genetic algorithm. Then, the system voltage stability is analyzed to compare the different possible operating points using V-Q sensitivity analysis. The power losses, obtained for various operating points, are considered the third objective function. Finally, these 3 aspects of cost, voltage stability, and power losses are …

Change Detection Without Difference Image Computation Based On Multiobjective Cost Function Optimization, Turgay Çeli̇k, Zeki̇ Yetgi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we propose a novel method for unsupervised change detection in multi-temporal satellite images by using multiobjective cost function optimization via genetic algorithm (GA). The spatial image grid of the input multi-temporal satellite images is divided into two distinct regions, representing ``changed'' and ``unchanged'' regions between input images, via the intermediate change detection mask produced by the GA. The dissimilarity of pixels of ``changed'' regions and similarity of pixels of ``unchanged'' regions between input multi-temporal images are measured using image quality metrics which consider correlation, spectral distortion, radiometric distortion, and contrast distortion. The contextual information of each pixel …

Genetic Algorithms Vs. Simulated Annealing: A Comparison Of Approaches For Solving The Circuit Partitioning Problem, Theodore W. Manikas, James T. Cain

Computer Science and Engineering Research

An important stage in circuit design is placement, where components are assigned to physical locations on a chip. A popular contemporary approach for placement is the use of simulated annealing. While this approach has been shown to produce good placement solutions, recent work in genetic algorithms has produced promising results. The purpose of this study is to determine which approach will result in better placement solutions.

A simplified model of the placement problem, circuit partitioning, was tested on three circuits with both a genetic algorithm and a simulated annealing algorithm. When compared with simulated annealing, the genetic algorithm was found …