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Computer and Computational Sciences (ICCCS)

作者： Vedik, B. Chandel, Ashwani Kumar NIT Hamirpur Dept Elect Engn Hamirpur India

ISBN: (纸本)9781479918195

In the present paper, gravitational search algorithm in conjunction with weighted least square method has been proposed to solve state estimation (PSSE) problem by considering both conventional and PMU measurements. The proposed method considers the current phasors in polar coordinates and alleviates the ill-conditioned problem caused during flat start. The proposed algorithm has been tested on IEEE-14 and IEEE-30 bus test systems. The effectiveness of the proposed method has been compared with conventional weighted least squares method. The results reveal that the proposed method yield towards the optimum solution and it is far better than WLS method in terms of accuracy.

关键词： conventional measurements gravitational search algorithm phasor measurement unit power system state estimation weighted least square

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Priority based optimization of PID controller for automatic voltage regulator system using gravitational search algorithm

Priority based optimization of PID controller for automatic ...

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International Conference on Recent Developments in Control Automation and Power Engineering (RDCAPE)

作者： Kumar, Ashish Shankar, Gauri Indian Sch Mines Dhanbad Dept Elect Engn Dhanbad 826004 Jharkhand India

ISBN: (纸本)9781479972470

In this paper, an optimal design PID controller for automatic voltage regulator (AVR) using gravitational search algorithm (GSA) is presented. In the present work, a priority based error magnification technique is used. This technique emphasizes on the need of prioritizing certain characteristics of transient response of an AVR system. The proposed method consists of a fitness function that helps GSA to search optimum value of PID controller parameters quickly and efficiently. Results obtained using GSA is compared with that other optimization techniques such as genetic algorithm (GA) and particle swarm optimization (PSO). It is observed that the results obtained using GSA is the best in comparison to the results obtained using GA and PSO.

关键词： Automatic voltage regulator gravitational search algorithm PID controller priority factor

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A New algorithm for Data Clustering Based on gravitational search algorithm and Genetic Operators

A New Algorithm for Data Clustering Based on Gravitational S...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Nikbakht, Hamed Mirvaziri, Hamid Shahid Bahonar Univ Kerman Dept Comp Engn Kerman Iran Shahid Bahonar Univ Kerman Young Researchers Assoc Kerman Iran

ISBN: (纸本)9781479988181

Data clustering is a crucial technique in data mining that is used in many applications. In this paper, a new clustering algorithm based on gravitational search algorithm (GSA) and genetic operators is proposed. The local search solution is utilized throw the global search to avoid getting stock in local optima. The GSA is a new approach to solve optimization problem that inspired by Newtonian law of gravity. We compared the performances of the proposed method with some well-known clustering algorithms on five benchmark dataset from UCI Machine Learning Repository. The experimental results show that our approach outperforms other algorithms and has better solution in all datasets.

关键词： clustering gravitational search algorithm Genetic Operators local search

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Evolving an Adaptive Artificial Neural Network with a gravitational search algorithm 7th

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7th KES International Conference on Intelligent Decision Technologies (KES-IDT)

作者： Tan, Shing Chiang Lim, Chee Peng Multimedia Univ Cyberjaya Malaysia Deakin Univ Geelong Vic Australia

ISBN: (纸本)9783319198576;9783319198569

In this paper, a supervised fuzzy adaptive resonance theory neural network, i.e., Fuzzy ARTMAP (FAM), is integrated with a heuristic gravitational search algorithm (GSA) that is inspired from the laws of Newtonian gravity. The proposed FAM-GSA model combines the unique features of both constituents to perform data classification. The classification performance of FAM-GSA is benchmarked against other state-of-art machine learning classifiers using an artificially generated data set and two real data sets from different domains. Comparatively, the empirical results indicate that FAM-GSA generally is able to achieve a better classification performance with a parsimonious network size, but with the expense of a higher computational load.

关键词： Adaptive resonance theory Artificial neural network Supervised learning gravitational search algorithm Data classification

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Training Feedforward Neural Networks using Hybrid Flower Pollination-gravitational search algorithm 1

Training Feedforward Neural Networks using Hybrid Flower Pol...

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International Conference On Futuristic Trends in Computational Analysis and Knowledge Management

作者： Chakraborty, Dwaipayan Saha, Sankhadip Maity, Samaresh Netaji Subhash Engn Coll Gria Dept Elect & Instru Kolkata India Netaji Subhash Engn Coll Graia Dept Elect Engn Kolkata India

ISBN: (纸本)9781479984336

Error minimization using conventional back-propagation algorithm for training feed forward neural network ( FNN) suffers from problems like slow convergence and local minima trap. Here in this paper gradient free optimization is used for error minimization to avoid local minima. Hence we introduce a new hybrid algorithm integrating the concepts of physics inspired gravitational search algorithm and biology inspired flower pollination algorithm. gravitational search algorithm is a novel meta-heuristic optimization method based on the Newtonian law of gravity and mass interaction, whereas flower pollination algorithm is an intriguing process based on the pollination characteristics of flowering plants. gravitational search algorithm efficiently evaluates global optimum but it suffers from slow searching speed in the last iterations. Flower pollination algorithm exhibits faster searching but suffers from local minima due to the switch probability. Experimental results show that hybrid FP-GSA outperforms both FPA and GSA for training FNNs in terms of classification accuracy.

关键词： Flower Pollination algorithm gravitational search algorithm feed forward neural network optimization meta-heuristic

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Optimal Design of Wideband Digital Integrators Using gravitational search algorithm

Optimal Design of Wideband Digital Integrators Using Gravita...

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International Conference on Computing, Communication & Automation (ICCCA)

作者： Kumar, Manjeet Rawat, Tarun Kumar Singh, Atul Anshuman Mittal, Aviral Jain, Aman Netaji Subhas Inst Technol Div Elect & Commun Engn Delhi 110078 India

ISBN: (纸本)9781479988907

In this paper a new heuristic algorithm called gravitational search algorithm (GSA), is employed for the optimization of the filter coefficients of infinite impulse response (IIR) integrator design problem. GSA is a population based algorithm formulated on the principles of Newtonian gravity and law of motion. GSA employs the interaction of masses in an isolated system directed by law of Newtonian gravity and motion. The efficiency of GSA lies in the fact that it presents a suitable transition between exploration and exploitation stage and has a very quick convergence rate. The simulation results have been compared to the well accepted conventional integrator design techniques such as segment rule and trapezoidal (TPZ) methods, and evolutionary optimization techniques like genetic algorithm (GA), simulated annealing (SA), pole zero placement (PZ), particle swarm optimization (PSO), fletcher Powell (FP) and minimax, pole, zero and constant optimization (MPZCO) methods. The results unleash the potential of GSA in obtaining improved magnitude error and comparable or improved phase error.

关键词： gravitational search algorithm Evolutionary Optimization Digital Integrator

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Communication Satellite Link Budget Optimization Using gravitational search algorithm 3

Communication Satellite Link Budget Optimization Using Gravi...

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INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING & INFORMATION TECHNOLOGY (CEIT)

作者： Berrezzoug, S. Bendimerad, F. T. Boudjemai, A. CDS Space Technol Res Div BP 4065 Ibn Rochd Usto Oran Algeria Tlemcen Univ Lab Telecommun LTT Dept Elect Engn Pole Chetouane 13000 Algeria

ISBN: (纸本)9781479982134

A communication satellites play a large role in the socio-economic development of a country, however the satellite communication system design trade-offs increase with the complexity of the payload requirements. For reliable and dynamic in-orbit satellite operations, the payload receiver must have the capability to adapt to emergent mission and post-mission application requirements. The communication link between a satellite and the Earth Station (ES) is exposed to a lot of impairments such as noise, rain and atmospheric attenuations. It is also prone to loss such as those resulting from antenna misalignment and polarization. It is therefore crucial to design for all possible attenuation scenarios before the satellite is deployed. This paper presents the fundamentals of a satellite link budget of generic communication satellite. Furthermore, a system engineering case study for a satellite communications mission is presented. Adopting this design philosophy in future space satellite payload module promises stable, economical, optimal, broadband and adaptive space operations. In this paper a gravitational search algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion.

关键词： gravitational search algorithm Optimization Communication Satellite link budget Earth station

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FUZZY CLUSTERING USING gravitational search algorithm

FUZZY CLUSTERING USING GRAVITATIONAL SEARCH ALGORITHM

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作者： Himanshu Kaul Delhi Technological University

学位级别：硕士

Clustering is a key activity in numerous data mining applications such as information retrieval, text mining, image segmentation, etc. This research work proposes a clustering approach, Fuzzy-GSA, based on gravitational search algorithm (GSA) with parameter adaptation using fuzzy inference system. The parameter α, used in the calculation of the gravitational constant G, plays a crucial role in guiding the search process in GSA. Lower values of α increase search exploration, whereas higher values increase search exploitation. In the proposed Fuzzy-GSA approach, fuzzy inference rules are used to control the value of parameter α in GSA. The performance of the Fuzzy-GSA algorithm is evaluated against four benchmark datasets. The results illustrate that the Fuzzy-GSA approach attains the highest quality clustering when compared with several other clustering algorithms.

关键词： Clustering Fuzzy-GSA gravitational search algorithm Fuzzy Inference System

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gravitational search algorithm for Optimal Distributed Generation Operation in Autonomous Network

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第10期39卷 7183-7188页

作者： Jamian, J. J. Mustafa, M. W. Mokhlis, H. Baharudin, M. A. Abdilahi, A. M. Univ Teknol Malaysia Skudai 81310 Malaysia Univ Malaya Kuala Lumpur 50603 Malaysia

The gravitational search algorithm (GSA) has been used in this research to optimize output of the multiple distributed generator (DG) units in autonomous distribution network. With the optimal operation of DGs, the voltage profile can be improved, thus reducing the power losses in the system. The performance of GSA is compared with an established paper, which uses the genetic algorithm (GA) in analysing similar problem. The results show that the GSA has superior performance in finding the optimal DG output compared to the GA technique, either in terms of power loss as well as the voltage profile. Furthermore, the consistency of the proposed GSA method is proven by the small standard deviation value obtained from 20 repetitions of the same analysis.

关键词： Autonomous network Distributed generation gravitational search algorithm Power loss minimization Standard deviation

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A novel robust hybrid gravitational search algorithm for reusable launch vehicle approach and landing trajectory optimization

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Neurocomputing 2015年 162卷 116-127页

作者： Zikang Su Honglun Wang School of Automation Science and Electrical Engineering Beihang University 100191 Beijing China School of Advanced Engineering Beihang University 100191 Beijing China The Science and Technology on Aircraft Control Laboratory Beihang University 100191 Beijing China

The approach and landing (A&L) trajectory optimization is a critical problem for secure flight of reusable launch vehicle (RLV). In this paper, the A&L is divided into two sub-phases, glide phase and flare phase respectively. The flare phase is designed firstly based on the desired touchdown (TD) states. Then, the glide phase is optimized using a proposed novel robust hybrid algorithm that combines advantages of the gravitational search algorithm (GSA) and gauss pseudospectral method (GPM). In the proposed hybrid algorithm, an improved GSA (IGSA) is presented to enhance the convergence speed and the global search ability, by adopting the elite memory reservation strategy and an adaptive gravitational constant adaption with individual optimum fitness feedback. At the beginning stage of search process, an initialization generator is constructed to find an optimum solution with IGSA, due to its strong global search ability and robustness to the initial values. When the change in fitness value satisfies the predefined value, the IGSA is replaced by the GPM to accelerate the search process and to get an accurate optimum solution. Finally, the Monte Carlo simulation results are analyzed in detail, which demonstrate the proposed method is practicable. The comparison with GSA and GPM shows that the hybrid algorithm has better performance in terms of convergence speed, robustness and accuracy for solving the RLV A&L trajectory optimization problem.

关键词： Trajectory optimization gravitational search algorithm Gauss pseudospectral method RLV Approach and landing Optimal control

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