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Facing the classification of binary problems with a GSA-SVM hybrid system

MATHEMATICAL AND COMPUTER MODELLING 2013年第1-2期57卷 270-278页

作者： Sarafrazi, Soroor Nezamabadi-pour, Hossein Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

This paper hybridizes the gravitational search algorithm (GSA) with support vector machine (SVM) and makes a novel GSA-SVM hybrid system to improve classification accuracy with an appropriate feature subset in binary problems. In order to simultaneously optimize the input feature subset selection and the SVM parameter setting, a discrete GSA is combined with a continuous-valued GSA in this system. We evaluate the proposed hybrid system on several UCI machine learning benchmark examples. The results show that the proposed approach is able to select the discriminating input features correctly and achieve high classification accuracy which is comparable to or better than well-known similar classifier systems. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Binary classification Support vector machine gravitational search algorithm Feature selection Parameter tuning

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gravitational particle swarm

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APPLIED MATHEMATICS AND COMPUTATION 2013年第17期219卷 9106-9117页

作者： Tsai, Hsing-Chih Tyan, Yaw-Yauan Wu, Yun-Wu Lin, Yong-Huang Natl Taiwan Univ Sci & Technol Dept Construct Engn Taipei Taiwan China Univ Technol Dept Civil Engn & Hazard Mitigat Design Hu Kou Taiwan China Univ Technol Dept Architecture Taipei Taiwan

Particle swarm optimization (PSO) is inspired by social behavior of bird flocking, gravitational search algorithm (GSA) is based on the law of gravity, and both of them are related to swarm intelligence (SI). gravitational particle swarm (GPS) is proposed where a GPS agent has attributes of GSA and PSO. GPS agents update their respective positions with PSO velocity and GSA acceleration. GPS agents, therefore, are able to exhibit PSO bird social and cognitive behaviors and motion in flight, while also reflecting the law of gravity of GSA. From results of 23 benchmark functions, GPS does significantly improve PSO and GSA, with noticeably marked improvements. This paper proposes GPS for hybridizing PSO and GSA due to the outstanding performance and interesting concepts embodied in the GPS. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Optimization Particle swarm optimization gravitational search algorithm gravitational particle swarm

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Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第1期26卷 314-326页

作者： Gonzalez-Alvarez, David L. Vega-Rodriguez, Miguel A. Gomez-Pulido, Juan A. Sanchez-Perez, Juan M. Univ Extremadura Dept Technol Comp & Commun ARCO Res Grp Escuela Politecn Caceres 10003 Spain

In recent years, a huge number of biological problems have been successfully addressed through computational techniques, among all these computational techniques we highlight metaheuristics. Also, most of these biological problems are directly related to genomic, studying the microorganisms, plants, and animals genomes. In this work, we solve a DNA sequence analysis problem called Motif Discovery Problem (MDP) by using two novel algorithms based on swarm intelligence: Artificial Bee Colony (ABC) and gravitational search algorithm (GSA). To guide the pattern search to solutions that have a better biological relevance, we have redefined the problem formulation and incorporated several biological constraints that should be satisfied by each solution. One of the most important characteristics of the problem definition is the application of multiobjective optimization (MOO), maximizing three conflicting objectives: motif length, support, and similarity. So, we have adapted our algorithms to the multiobjective context. This paper presents an exhaustive comparison of both multiobjective proposals on instances of different nature: real instances, generic instances, and instances generated according to a Markov chain. To analyze their operations we have used several indicators and statistics, comparing their results with those obtained by standard algorithms in multiobjective computation, and by 14 well-known biological methods. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Swarm intelligence Artificial bee colony gravitational search algorithm Deoxyribonucleic acid (DNA) Motif discovery Multiobjective optimization

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A simultaneous feature adaptation and feature selection method for content-based image retrieval systems

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KNOWLEDGE-BASED SYSTEMS 2013年第Feb.期39卷 85-94页

作者： Rashedi, Esmat Nezamabadi-pour, Hossein Saryazdi, Saeid Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

In content-based image retrieval (CBIR) applications, each database needs its corresponding parameter setting for feature extraction. However, most of the CBIR systems perform indexing by a set of fixed and pre-specific parameters. On the other hand, feature selection methods have currently gained considerable popularity to reduce semantic gap. In this regard, this paper is devoted to present a hybrid approach to reduce the semantic gap between low level visual features and high level semantics, through simultaneous feature adaptation and feature selection. In the proposed approach, a hybrid meta-heuristic swarm intelligence-based search technique, called mixed gravitational search algorithm (MGSA), is employed. Some feature extraction parameters (i.e. the parameters of a 6-tap parameterized orthogonal mother wavelet in texture features and quantization levels in color histogram) are optimized to reach a maximum precision of the CBIR systems. Meanwhile, feature subset selection is done for the same purpose. A comparative experimental study with the conventional CBIR system is reported on a database of 1000 images. The obtained results confirm the effectiveness of the proposed adaptive indexing method in the field of CBIR. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Image retrieval Adaptive feature extraction Feature selection Parameterized wavelet gravitational search algorithm

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An Optimal Prediction based Reversible Image Watermarking In Hadamard Domain

An Optimal Prediction based Reversible Image Watermarking In...

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8th Iranian Conference on Machine Vision and Image Processing (MVIP)

作者： Pakdaman, Zahra Saryazdi, Saeid Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

ISBN: (纸本)9781467361842

This paper presents a reversible watermarking scheme based on a reversible Hadamarh Transform. In the proposed method, the watermark is embedded using the prediction error of Hadamard coefficients. To achieve a more accurate prediction, a gravitational search algorithm (GSA) is used to optimize the prediction coefficients. The proposed method does not need any location map. This property leads to increase the capacity as well as the quality of the watermarked image. To evaluate the performance of the proposed method, a comparative experiment with some well-known reversible methods is performed. The obtained results confirm the efficiency of the proposed method.

关键词： reversible watermarking Reversible Hadamard Transform prediction error gravitational search algorithm

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Flow-Based Anomaly Detection Using Neural Network Optimized with GSA algorithm

Flow-Based Anomaly Detection Using Neural Network Optimized ...

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33rd IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Jadidi, Zahra Muthukkumarasamy, Vallipuram Sithirasenan, Elankayer Sheikhan, Mansour Griffith Univ Sch Informat & Commun Technol Gold Coast Qld Australia Islamic Azad Univ Fac Engn Dept Elect Engn South Tehran Branch Tehran Iran

ISBN: (纸本)9781479932474;9780769550237

Reliable high-speed networks are essential to provide quality services to ever growing Internet applications. A Network Intrusion Detection System (NIDS) is an important tool to protect computer networks from attacks. Traditional packet-based NIDSs are time-intensive as they analyze all network packets. A state-of-the-art NIDS should be able to handle a high volume of traffic in real time. Flow-based intrusion detection is an effective method for high speed networks since it inspects only packet headers. The existence of new attacks in the future is another challenge for intrusion detection. Anomaly-based intrusion detection is a well-known method capable of detecting unknown attacks. In this paper, we propose a flow-based anomaly detection system. Artificial Neural Network (ANN) is an important approach for anomaly detection. We used a Multi-Layer Perceptron (MLP) neural network with one hidden layer. We investigate the use of a gravitational search algorithm (GSA) in optimizing interconnection weights of a MLP network. Our proposed GSA-based flow anomaly detection system (GFADS) is trained with a flow-based data set. The trained system can classify benign and malicious flows with 99.43% accuracy. We compare the performance of GSA with traditional gradient descent training algorithms and a particle swarm optimization (PSO) algorithm. The results show that GFADS is effective in flow-based anomaly detection. Finally, we propose a four-feature subset as the optimal set of features.

关键词： Flow-based anomaly detection Multi-layer Perceptron gravitational search algorithm

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Characterization of high-permeability streak in mature waterflooding reservoirs using pressure transient analysis

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Journal of Petroleum Science and Engineering 2013年 110卷 55-65页

作者： Qihong Feng Sen Wang Wei Zhang Yulong Song Shemin Song Department of Petroleum Engineering China University of Petroleum (East China) Qingdao Shandong 266580 China Huabei Oilfield Development Department China National Petroleum Corporation Renqiu Hebei 062552 China

The characterization of thief zone, which evolves from long-term waterflooding, has become imperative in the enhanced oil recovery process. As one typical kind of thief zone, pressure transient performance of high-permeability streak is analyzed in this work. A mathematical model is established for a well intersected by a high-permeability streak, and the solution in Laplace space is derived by Ozkan's source function. To ensure improved accuracy and better efficiency, the solution is inverted into real space numerically through de Hoog algorithm. Investigation of the pressure transient behavior indicates that the process can be divided into three periods: (1) the early-time flow period, which is comprised of streak storage-type flow and bilinear flow; however, it occurs too early to be of practical interest; (2) the linear flow from the formation to the streak, which is characterized by a half-slope straight line; (3) pseudo-radial flow, exhibited by a horizontal line on the derivative curve. The sensitivities of corresponding parameters have also been discussed. Furthermore, gravitational search algorithm (GSA) is successfully applied as a non-linear regression technology to match measured pressure with this model and to characterize high-permeability streak. Compared to previous technologies, this approach is more cost-effective and less time-consuming. Moreover, the quantitative characterization of high-permeability streak can play a very important role in the design of conformance control project. Thus this approach has been extensively employed in many China Oilfields. And field cases are also presented to substantiate its validity.

关键词： high-permeability streak pressure transient analysis thief zone source function gravitational search algorithm

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gravitational search algorithms in Fuzzy Control Systems Tuning

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IFAC Proceedings Volumes 2011年第1期44卷 13624-13629页

作者： Radu-Emil Precup Radu-Codruţ David Emil M. Petriu Stefan Preitl Mircea-Bogdan Rădac Department of Automation and Applied Informatics “Politehnica” University of Timisoara Bd. V. Parvan 2 RO-300223 Timisoara Romania (Tel: +40-256403229

Abstract This paper suggests the use of gravitational search algorithms (GSAs) in fuzzy control systems tuning. New GSAs are first offered on the basis of the modification of the depreciation equation of the gravitational constant with the iteration index and of an additional constraint regarding system's overshoot. The GSAs are next used in solving the optimization problems which minimize the discrete-time objective functions defined as the weighted sum of the squared control error and of the squared output sensitivity functions. The sensitivity functions are derived from the sensitivity models defined with respect to the parametric variations of the controlled plant such that to aim the parametric sensitivity reduction. The presentation focuses the representative case of Takagi-Sugeno PI-fuzzy controllers (PI-FCs) that controls a class of servo systems characterized by second-order linearized models with integral component. Discussions concerning the tuning of the PI-FC parameters in a case study are included.

关键词： gravitational search algorithm fuzzy control systems objective functions performance specifications servo systems

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A High-Speed, Performance-Optimization algorithm Based on a gravitational Approach

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COMPUTING IN SCIENCE & ENGINEERING 2012年第5期14卷 56-62页

作者： Naji, Hamid Reza Sohrabi, Mina Rashedi, Esmat Kerman Grad Univ Technol Coll Elect & Comp Engn Kerman Iran Int Ctr Sci High Technol & Environm Sci Kerman Iran Shahid Bahonar Univ Kerman Dept Elect Engn Youngers Res Inst Tehran Iran

gravitational search algorithms (GSAs) yield high performances in solving optimization problems, but require time-consuming computations for the total force on each mass which makes the speed of optimization very low. However, replacing a GSA's sequential approach with a multiagent system could improve a GSA's speed considerably while maintaining the high performance level.

关键词： Multiagent Systems Heuristic algorithms Optimization Acceleration Gravity Runtime Sequential Analysis Scientific Computing gravitational search algorithm GSA Optimization Gravity Multiagent Systems

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New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

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IEEE TRANSACTIONS ON POWER DELIVERY 2012年第1期27卷 140-146页

作者： Oba Ramos, Caio Cesar de Souza, Andre Nunes Falcao, Alexandre Xavier Papa, Joao Paulo Univ Sao Paulo Dept Elect Engn BR-05508970 Sao Paulo Brazil Univ Estadual Campinas Inst Comp BR-13083852 Sao Paulo Brazil UNESP Univ Estadual Paulista Dept Comp BR-17033360 Sao Paulo Brazil

Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.

关键词： Feature selection gravitational search algorithm harmony search nontechnical losses optimum-path forest particle swarm optimization pattern recognition

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