Search results for: Chaos Optimization Algorithm
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 4668

Search results for: Chaos Optimization Algorithm

3228 Parallelization and Optimization of SIFT Feature Extraction on Cluster System

Authors: Mingling Zheng, Zhenlong Song, Ke Xu, Hengzhu Liu

Abstract:

Scale Invariant Feature Transform (SIFT) has been widely applied, but extracting SIFT feature is complicated and time-consuming. In this paper, to meet the demand of the real-time applications, SIFT is parallelized and optimized on cluster system, which is named pSIFT. Redundancy storage and communication are used for boundary data to improve the performance, and before representation of feature descriptor, data reallocation is adopted to keep load balance in pSIFT. Experimental results show that pSIFT achieves good speedup and scalability.

Keywords: cluster, image matching, parallelization and optimization, SIFT.

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3227 Gaussian Process Model Identification Using Artificial Bee Colony Algorithm and Its Application to Modeling of Power Systems

Authors: Tomohiro Hachino, Hitoshi Takata, Shigeru Nakayama, Ichiro Iimura, Seiji Fukushima, Yasutaka Igarashi

Abstract:

This paper presents a nonparametric identification of continuous-time nonlinear systems by using a Gaussian process (GP) model. The GP prior model is trained by artificial bee colony algorithm. The nonlinear function of the objective system is estimated as the predictive mean function of the GP, and the confidence measure of the estimated nonlinear function is given by the predictive covariance of the GP. The proposed identification method is applied to modeling of a simplified electric power system. Simulation results are shown to demonstrate the effectiveness of the proposed method.

Keywords: Artificial bee colony algorithm, Gaussian process model, identification, nonlinear system, electric power system.

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3226 AES and ECC Mixed for ZigBee Wireless Sensor Security

Authors: Saif Al-alak, Zuriati Ahmed, Azizol Abdullah, Shamala Subramiam

Abstract:

In this paper, we argue the security protocols of ZigBee wireless sensor network in MAC layer. AES 128-bit encryption algorithm in CCM* mode is secure transferred data; however, AES-s secret key will be break within nearest future. Efficient public key algorithm, ECC has been mixed with AES to rescue the ZigBee wireless sensor from cipher text and replay attack. Also, the proposed protocol can parallelize the integrity function to increase system performance.

Keywords: AES, ECC, Multi-level security, ZigBee

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3225 Multi-VSS Scheme by Shifting Random Grids

Authors: Joy Jo-Yi Chang, Justie Su-Tzu Juan

Abstract:

Visual secret sharing (VSS) was proposed by Naor and Shamir in 1995. Visual secret sharing schemes encode a secret image into two or more share images, and single share image can’t obtain any information about the secret image. When superimposes the shares, it can restore the secret by human vision. Due to the traditional VSS have some problems like pixel expansion and the cost of sophisticated. And this method only can encode one secret image. The schemes of encrypting more secret images by random grids into two shares were proposed by Chen et al. in 2008. But when those restored secret images have much distortion, those schemes are almost limited in decoding. In the other words, if there is too much distortion, we can’t encrypt too much information. So, if we can adjust distortion to very small, we can encrypt more secret images. In this paper, four new algorithms which based on Chang et al.’s scheme be held in 2010 are proposed. First algorithm can adjust distortion to very small. Second algorithm distributes the distortion into two restored secret images. Third algorithm achieves no distortion for special secret images. Fourth algorithm encrypts three secret images, which not only retain the advantage of VSS but also improve on the problems of decoding.

Keywords: Visual cryptography, visual secret sharing, random grids, multiple, secret image sharing

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3224 Fast Generation of High-Performance Driveshafts: A Digital Approach to Automated Linked Topology and Design Optimization

Authors: Willi Zschiebsch, Alrik Dargel, Sebastian Spitzer, Philipp Johst, Robert Böhm, Niels Modler

Abstract:

In this article, we investigate an approach that digitally links individual development process steps by using the drive shaft of an aircraft engine as representative example of a fiber polymer composite. Such high-performance lightweight composite structures have many adjustable parameters that influence the mechanical properties. Only a combination of optimal parameter values can lead to energy efficient lightweight structures. The development tools required for the Engineering Design Process (EDP) are often isolated solutions and their compatibility with each other is limited. A digital framework is presented in this study, which allows individual specialised tools to be linked via the generated data in such a way that automated optimization across programs becomes possible. This is demonstrated using the example of linking geometry generation with numerical structural analysis. The proposed digital framework for automated design optimization demonstrates the feasibility of developing a complete digital approach to design optimization. The methodology shows promising potential for achieving optimal solutions in terms of mass, material utilization, eigenfrequency and deformation under lateral load with less development effort. The development of such a framework is an important step towards promoting a more efficient design approach that can lead to stable and balanced results.

Keywords: Digital Linked Process, composite, CFRP, multi-objective, EDP, NSGA-2, NSGA-3, TPE.

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3223 Heuristic Search Algorithm (HSA) for Enhancing the Lifetime of Wireless Sensor Networks

Authors: Tripatjot S. Panag, J. S. Dhillon

Abstract:

The lifetime of a wireless sensor network can be effectively increased by using scheduling operations. Once the sensors are randomly deployed, the task at hand is to find the largest number of disjoint sets of sensors such that every sensor set provides complete coverage of the target area. At any instant, only one of these disjoint sets is switched on, while all other are switched off. This paper proposes a heuristic search method to find the maximum number of disjoint sets that completely cover the region. A population of randomly initialized members is made to explore the solution space. A set of heuristics has been applied to guide the members to a possible solution in their neighborhood. The heuristics escalate the convergence of the algorithm. The best solution explored by the population is recorded and is continuously updated. The proposed algorithm has been tested for applications which require sensing of multiple target points, referred to as point coverage applications. Results show that the proposed algorithm outclasses the existing algorithms. It always finds the optimum solution, and that too by making fewer number of fitness function evaluations than the existing approaches.

Keywords: Coverage, disjoint sets, heuristic, lifetime, scheduling, wireless sensor networks, WSN.

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3222 Applying Fuzzy FP-Growth to Mine Fuzzy Association Rules

Authors: Chien-Hua Wang, Wei-Hsuan Lee, Chin-Tzong Pang

Abstract:

In data mining, the association rules are used to find for the associations between the different items of the transactions database. As the data collected and stored, rules of value can be found through association rules, which can be applied to help managers execute marketing strategies and establish sound market frameworks. This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth) to derive from fuzzy association rules. At first, we apply fuzzy partition methods and decide a membership function of quantitative value for each transaction item. Next, we implement FFP-growth to deal with the process of data mining. In addition, in order to understand the impact of Apriori algorithm and FFP-growth algorithm on the execution time and the number of generated association rules, the experiment will be performed by using different sizes of databases and thresholds. Lastly, the experiment results show FFPgrowth algorithm is more efficient than other existing methods.

Keywords: Data mining, association rule, fuzzy frequent patterngrowth.

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3221 Contourlet versus Wavelet Transform for a Robust Digital Image Watermarking Technique

Authors: Ibrahim A. El rube, Mohamad Abou El Nasr , Mostafa M. Naim, Mahmoud Farouk

Abstract:

In this paper, a watermarking algorithm that uses the wavelet transform with Multiple Description Coding (MDC) and Quantization Index Modulation (QIM) concepts is introduced. Also, the paper investigates the role of Contourlet Transform (CT) versus Wavelet Transform (WT) in providing robust image watermarking. Two measures are utilized in the comparison between the waveletbased and the contourlet-based methods; Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NCC). Experimental results reveal that the introduced algorithm is robust against different attacks and has good results compared to the contourlet-based algorithm.

Keywords: image watermarking; discrete wavelet transform, discrete contourlet transform, multiple description coding, quantization index modulation

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3220 Comparative study of the Genetic Algorithms and Hessians Method for Minimization of the Electric Power Production Cost

Authors: L. Abdelmalek, M. Zerikat, M. Rahli

Abstract:

In this paper, we present a comparative study of the genetic algorithms and Hessian-s methods for optimal research of the active powers in an electric network of power. The objective function which is the performance index of production of electrical energy is minimized by satisfying the constraints of the equality type and inequality type initially by the Hessian-s methods and in the second time by the genetic Algorithms. The results found by the application of AG for the minimization of the electric production costs of power are very encouraging. The algorithms seem to be an effective technique to solve a great number of problems and which are in constant evolution. Nevertheless it should be specified that the traditional binary representation used for the genetic algorithms creates problems of optimization of management of the large-sized networks with high numerical precision.

Keywords: Genetic algorithm, Flow of optimum loadimpedances, Hessians method, Optimal distribution.

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3219 Loop Back Connected Component Labeling Algorithm and Its Implementation in Detecting Face

Authors: A. Rakhmadi, M. S. M. Rahim, A. Bade, H. Haron, I. M. Amin

Abstract:

In this study, a Loop Back Algorithm for component connected labeling for detecting objects in a digital image is presented. The approach is using loop back connected component labeling algorithm that helps the system to distinguish the object detected according to their label. Deferent than whole window scanning technique, this technique reduces the searching time for locating the object by focusing on the suspected object based on certain features defined. In this study, the approach was also implemented for a face detection system. Face detection system is becoming interesting research since there are many devices or systems that require detecting the face for certain purposes. The input can be from still image or videos, therefore the sub process of this system has to be simple, efficient and accurate to give a good result.

Keywords: Image processing, connected components labeling, face detection.

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3218 Controllability of Efficiency of Antiviral Therapy in Hepatitis B Virus Infections

Authors: Shyam S.N. Perera

Abstract:

An optimal control problem for a mathematical model of efficiency of antiviral therapy in hepatitis B virus infections is considered. The aim of the study is to control the new viral production, block the new infection cells and maintain the number of uninfected cells in the given range. The optimal controls represent the efficiency of antiviral therapy in inhibiting viral production and preventing new infections. Defining the cost functional, the optimal control problem is converted into the constrained optimization problem and the first order optimality system is derived. For the numerical simulation, we propose the steepest descent algorithm based on the adjoint variable method. A computer program in MATLAB is developed for the numerical simulations.

Keywords: Virus infection model, Optimal control, Adjoint system, Steepest descent

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3217 Resource-Constrained Heterogeneous Workflow Scheduling Algorithm for Heterogeneous Computing Clusters

Authors: Lei Wang, Jiahao Zhou

Abstract:

The development of heterogeneous computing clusters provides robust computational support for large-scale workflows, commonly seen in domains such as scientific computing and artificial intelligence. However, the tasks within these large-scale workflows are increasingly heterogeneous, exhibiting varying demands on computing resources. This shift necessitates the integration of resource-constrained considerations into the workflow scheduling problem on heterogeneous computing platforms. In this study, we propose a scheduling algorithm designed to minimize the makespan under heterogeneous constraints, employing a greedy strategy to effectively address the scheduling challenges posed by heterogeneous workflows. We evaluate the performance of the proposed algorithm using randomly generated heterogeneous workflows and a corresponding heterogeneous computing platform. The experimental results demonstrate a 15.2% improvement in performance compared to existing state-of-the-art methods.

Keywords: Heterogeneous Computing, Workflow Scheduling, Constrained Resources, Minimal Makespan.

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3216 Neural Networks and Particle Swarm Optimization Based MPPT for Small Wind Power Generator

Authors: Chun-Yao Lee, Yi-Xing Shen, Jung-Cheng Cheng, Yi-Yin Li, Chih-Wen Chang

Abstract:

This paper proposes the method combining artificial neural network (ANN) with particle swarm optimization (PSO) to implement the maximum power point tracking (MPPT) by controlling the rotor speed of the wind generator. First, the measurements of wind speed, rotor speed of wind power generator and output power of wind power generator are applied to train artificial neural network and to estimate the wind speed. Second, the method mentioned above is applied to estimate and control the optimal rotor speed of the wind turbine so as to output the maximum power. Finally, the result reveals that the control system discussed in this paper extracts the maximum output power of wind generator within the short duration even in the conditions of wind speed and load impedance variation.

Keywords: Maximum power point tracking, artificial neuralnetwork, particle swarm optimization.

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3215 Dynamic Routing to Multiple Destinations in IP Networks using Hybrid Genetic Algorithm (DRHGA)

Authors: K. Vijayalakshmi, S. Radhakrishnan

Abstract:

In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate.

Keywords: Dynamic Group membership change, Hybrid Genetic Algorithm, Link / node failure, QoS Parameters.

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3214 Error Effects on SAR Image Resolution using Range Doppler Imaging Algorithm

Authors: Su Su Yi Mon, Fang Jiancheng

Abstract:

Synthetic Aperture Radar (SAR) is an imaging radar form by taking full advantage of the relative movement of the antenna with respect to the target. Through the simultaneous processing of the radar reflections over the movement of the antenna via the Range Doppler Algorithm (RDA), the superior resolution of a theoretical wider antenna, termed synthetic aperture, is obtained. Therefore, SAR can achieve high resolution two dimensional imagery of the ground surface. In addition, two filtering steps in range and azimuth direction provide accurate enough result. This paper develops a simulation in which realistic SAR images can be generated. Also, the effect of velocity errors in the resulting image has also been investigated. Taking some velocity errors into account, the simulation results on the image resolution would be presented. Most of the times, algorithms need to be adjusted for particular datasets, or particular applications.

Keywords: Synthetic Aperture Radar (SAR), Range Doppler Algorithm (RDA), Image Resolution.

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3213 Evolutionary Program Based Approach for Manipulator Grasping Color Objects

Authors: Y. Harold Robinson, M. Rajaram, Honey Raju

Abstract:

Image segmentation and color identification is an important process used in various emerging fields like intelligent robotics. A method is proposed for the manipulator to grasp and place the color object into correct location. The existing methods such as PSO, has problems like accelerating the convergence speed and converging to a local minimum leading to sub optimal performance. To improve the performance, we are using watershed algorithm and for color identification, we are using EPSO. EPSO method is used to reduce the probability of being stuck in the local minimum. The proposed method offers the particles a more powerful global exploration capability. EPSO methods can determine the particles stuck in the local minimum and can also enhance learning speed as the particle movement will be faster.

Keywords: Color information, EPSO, hue, saturation, value (HSV), image segmentation, particle swarm optimization (PSO). Active Contour, GMM.

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3212 Optimal Embedded Generation Allocation in Distribution System Employing Real Coded Genetic Algorithm Method

Authors: Mohd Herwan Sulaiman, Omar Aliman, Siti Rafidah Abdul Rahim

Abstract:

This paper proposes a new methodology for the optimal allocation and sizing of Embedded Generation (EG) employing Real Coded Genetic Algorithm (RCGA) to minimize the total power losses and to improve voltage profiles in the radial distribution networks. RCGA is a method that uses continuous floating numbers as representation which is different from conventional binary numbers. The RCGA is used as solution tool, which can determine the optimal location and size of EG in radial system simultaneously. This method is developed in MATLAB. The effect of EG units- installation and their sizing to the distribution networks are demonstrated using 24 bus system.

Keywords: Embedded generation (EG), load flow study, optimal allocation, real coded genetic algorithm (RCGA).

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3211 Wavelet-Based Despeckling of Synthetic Aperture Radar Images Using Adaptive and Mean Filters

Authors: Syed Musharaf Ali, Muhammad Younus Javed, Naveed Sarfraz Khattak

Abstract:

In this paper we introduced new wavelet based algorithm for speckle reduction of synthetic aperture radar images, which uses combination of undecimated wavelet transformation, wiener filter (which is an adaptive filter) and mean filter. Further more instead of using existing thresholding techniques such as sure shrinkage, Bayesian shrinkage, universal thresholding, normal thresholding, visu thresholding, soft and hard thresholding, we use brute force thresholding, which iteratively run the whole algorithm for each possible candidate value of threshold and saves each result in array and finally selects the value for threshold that gives best possible results. That is why it is slow as compared to existing thresholding techniques but gives best results under the given algorithm for speckle reduction.

Keywords: Brute force thresholding, directional smoothing, direction dependent mask, undecimated wavelet transformation.

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3210 Robust Parameter and Scale Factor Estimation in Nonstationary and Impulsive Noise Environment

Authors: Zoran D. Banjac, Branko D. Kovacevic

Abstract:

The problem of FIR system parameter estimation has been considered in the paper. A new robust recursive algorithm for simultaneously estimation of parameters and scale factor of prediction residuals in non-stationary environment corrupted by impulsive noise has been proposed. The performance of derived algorithm has been tested by simulations.

Keywords: Adaptive filtering, Non-Gaussian filtering, Robustestimation, Scale factor estimation.

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3209 A Modified Genetic Based Technique for Solving the Power System State Estimation Problem

Authors: A. A. Hossam-Eldin, E. N. Abdallah, M. S. El-Nozahy

Abstract:

Power system state estimation is the process of calculating a reliable estimate of the power system state vector composed of bus voltages' angles and magnitudes from telemetered measurements on the system. This estimate of the state vector provides the description of the system necessary for the operation and security monitoring. Many methods are described in the literature for solving the state estimation problem, the most important of which are the classical weighted least squares method and the nondeterministic genetic based method; however both showed drawbacks. In this paper a modified version of the genetic algorithm power system state estimation is introduced, Sensitivity of the proposed algorithm to genetic operators is discussed, the algorithm is applied to case studies and finally it is compared with the classical weighted least squares method formulation.

Keywords: Genetic algorithms, ill-conditioning, state estimation, weighted least squares.

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3208 Solutions to Probabilistic Constrained Optimal Control Problems Using Concentration Inequalities

Authors: Tomoaki Hashimoto

Abstract:

Recently, optimal control problems subject to probabilistic constraints have attracted much attention in many research field. Although probabilistic constraints are generally intractable in optimization problems, several methods haven been proposed to deal with probabilistic constraints. In most methods, probabilistic constraints are transformed to deterministic constraints that are tractable in optimization problems. This paper examines a method for transforming probabilistic constraints into deterministic constraints for a class of probabilistic constrained optimal control problems.

Keywords: Optimal control, stochastic systems, discrete-time systems, probabilistic constraints.

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3207 Optimization of a Hybrid Wind-Pv-Diesel Standalone System: Case Chlef, Algeria

Authors: T. Tahri, A. Bettahar, M. Douani

Abstract:

In this work, an attempt is made to design an optimal wind/pv/diesel hybrid power system for a village of Ain Merane, Chlef, Algeria, where the wind speed and solar radiation measurements were made. The aim of this paper is the optimization of a hybrid wind/solar/diesel system applied in term of technical and economic feasibility by simulation using HOMER. A comparison was made between the performance of wind/pv/diesel system and the classic connecting system.

Keywords: Chlef-Algeria, Homer, Renewable energy, Wind-pvdiesel hybrid system.

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3206 An Algorithm for Estimating the Stable Operation Conditions of the Synchronous Motor of the Ore Mill Electric Drive

Authors: M. Baghdasaryan, A. Sukiasyan

Abstract:

An algorithm for estimating the stable operation conditions of the synchronous motor of the ore mill electric drive is proposed. The stable operation conditions of the synchronous motor are revealed, taking into account the estimation of the q angle change and the technological factors. The stability condition obtained allows to ensure the stable operation of the motor in the synchronous mode, taking into account the nonlinear character of the mill loading. The developed algorithm gives an opportunity to present the undesirable phenomena, arising in the electric drive system. The obtained stability condition can be successfully applied for the optimal control of the electromechanical system of the mill.

Keywords: Electric drive, synchronous motor, ore mill, stability, technological factors.

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3205 Maximum Power Point Tracking by ANN Controller for a Standalone Photovoltaic System

Authors: K. Ranjani, M. Raja, B. Anitha

Abstract:

In this paper, ANN controller for maximum power point tracking of photovoltaic (PV) systems is proposed and PV modeling is discussed. Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point. ANN controller with hill-climbing algorithm offers fast and accurate converging to the maximum operating point during steady-state and varying weather conditions compared to conventional hill-climbing. The proposed algorithm gives a good maximum power operation of the PV system. Simulation results obtained are presented and compared with the conventional hill-climbing algorithm. Simulation results show the effectiveness of the proposed technique.

Keywords: Artificial neural network (ANN), hill-climbing, maximum power-point tracking (MPPT), photovoltaic.

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3204 A Technique for Improving the Performance of Median Smoothers at the Corners Characterized by Low Order Polynomials

Authors: E. Srinivasan, D. Ebenezer

Abstract:

Median filters with larger windows offer greater smoothing and are more robust than the median filters of smaller windows. However, the larger median smoothers (the median filters with the larger windows) fail to track low order polynomial trends in the signals. Due to this, constant regions are produced at the signal corners, leading to the loss of fine details. In this paper, an algorithm, which combines the ability of the 3-point median smoother in preserving the low order polynomial trends and the superior noise filtering characteristics of the larger median smoother, is introduced. The proposed algorithm (called the combiner algorithm in this paper) is evaluated for its performance on a test image corrupted with different types of noise and the results obtained are included.

Keywords: Image filtering, detail preservation, median filters, nonlinear filters, order statistics filtering, Rank order filtering.

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3203 High Quality Speech Coding using Combined Parametric and Perceptual Modules

Authors: M. Kulesza, G. Szwoch, A. Czyżewski

Abstract:

A novel approach to speech coding using the hybrid architecture is presented. Advantages of parametric and perceptual coding methods are utilized together in order to create a speech coding algorithm assuring better signal quality than in traditional CELP parametric codec. Two approaches are discussed. One is based on selection of voiced signal components that are encoded using parametric algorithm, unvoiced components that are encoded perceptually and transients that remain unencoded. The second approach uses perceptual encoding of the residual signal in CELP codec. The algorithm applied for precise transient selection is described. Signal quality achieved using the proposed hybrid codec is compared to quality of some standard speech codecs.

Keywords: CELP residual coding, hybrid codec architecture, perceptual speech coding, speech codecs comparison.

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3202 Social, Group and Individual Mind extracted from Rule Bases of Multiple Agents

Authors: P. Cermak

Abstract:

This paper shows possibility of extraction Social, Group and Individual Mind from Multiple Agents Rule Bases. Types those Rule bases are selected as two fuzzy systems, namely Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are describing (modeling) agent behavior. Modifying of agent behavior in the time varying environment will be provided by learning fuzzyneural networks and optimization of their parameters with using genetic algorithms in development system FUZNET. Finally, extraction Social, Group and Individual Mind from Multiple Agents Rule Bases are provided by Cognitive analysis and Matching criterion.

Keywords: Mind, Multi-agent system, Cognitive analysis, Fuzzy system, Neural network, Genetic algorithm, Rule base.

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3201 Impact of the Electricity Market Prices on Energy Storage Operation during the COVID-19 Pandemic

Authors: Marin Mandić, Elis Sutlović, Tonći Modrić, Luka Stanić

Abstract:

With the restructuring and deregulation of the power system, storage owners, generation companies or private producers can offer their multiple services on various power markets and earn income in different types of markets, such as the day-ahead, real-time, ancillary services market, etc. During the COVID-19 pandemic, electricity prices, as well as ancillary services prices, increased significantly. The optimization of the energy storage operation was performed using a suitable model for simulating the operation of a pumped storage hydropower plant under market conditions. The objective function maximizes the income earned through energy arbitration, regulation-up, regulation-down and spinning reserve services. The optimization technique used for solving the objective function is mixed integer linear programming (MILP). In numerical examples, the pumped storage hydropower plant operation has been optimized considering the already achieved hourly electricity market prices from Nord Pool for the pre-pandemic (2019) and the pandemic (2020 and 2021) years. The impact of the electricity market prices during the COVID-19 pandemic on energy storage operation is shown through the analysis of income, operating hours, reserved capacity and consumed energy for each service. The results indicate the role of energy storage during a significant fluctuation in electricity and services prices.

Keywords: Electrical market prices, electricity market, energy storage optimization, mixed integer linear programming, MILP, optimization.

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3200 Capacitor Placement in Distribution Systems Using Simulating Annealing (SA)

Authors: Esmail Limouzade, Mahmood.Joorabian, Najaf Hedayat

Abstract:

This paper undertakes the problem of optimal capacitor placement in a distribution system. The problem is how to optimally determine the locations to install capacitors, the types and sizes of capacitors to he installed and, during each load level,the control settings of these capacitors in order that a desired objective function is minimized while the load constraints,network constraints and operational constraints (e.g. voltage profile) at different load levels are satisfied. The problem is formulated as a combinatorial optimization problem with a nondifferentiable objective function. Four solution mythologies based on algorithms (GA),tabu search (TS), and hybrid GA-SA algorithms are presented.The solution methodologies are preceded by a sensitivity analysis to select the candidate capacitor installation locations.

Keywords: Genetic Algorithm (GA) , capacitor placement, voltage profile, network losses, Simulated Annealing, distribution network.

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3199 A Hybrid Approach for Selection of Relevant Features for Microarray Datasets

Authors: R. K. Agrawal, Rajni Bala

Abstract:

Developing an accurate classifier for high dimensional microarray datasets is a challenging task due to availability of small sample size. Therefore, it is important to determine a set of relevant genes that classify the data well. Traditionally, gene selection method often selects the top ranked genes according to their discriminatory power. Often these genes are correlated with each other resulting in redundancy. In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately. A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1. The proposed method provides better results in comparison to the results found in the literature in terms of both classification accuracy and number of genes selected.

Keywords: Gene selection, genetic algorithm, microarray datasets, multi-class SVM.

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