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Genetic and Evolutionary Computation Conference (GECCO)

作者： Rothwell, Alex Aleti, Aldeida Monash Univ Caulfield Vic Australia

ISBN: (纸本)9781450349390

gravitational search algorithm (GSA) is a population-based optimisation technique that was originally developed to deal with high-dimensional search spaces. In recent years, GSA has been successfully applied to a wide range of problems. At every iteration, the algorithm calculates the gravitational force of each solution with respect to all other solutions, which has combinatorial complexity. In this paper, we propose an efficient way for calculating the force component of each solution, reducing the complexity of GSA from O(N-2) to O(N log (N)), where N is the population size. The experimental evaluation shows that the new algorithm is computationally efficient and cost effective.

关键词： gravitational search algorithm Barnes-Hut algorithm

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gravitational search algorithm combined with P&O method for MPPT in PV systems

Gravitational search algorithm combined with P&O method for ...

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IEEE Annual India Conference (INDICON)

作者： Sundareswaran, K. Vigneshkumar, V. Simon, Sishaj P. Nayak, P. Srinivasa Rao Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781509036462

This paper proposes a novel maximum power point tracking (MPPT) algorithm for photovoltaic (PV) power generation systems under non-uniform illumination. The proposed MPPT algorithm is composed of gravitational search algorithm (GSA) and traditional perturb and observe (P&O) method. In the initial stages of tracking, the power-voltage (P-V) curve is scanned through GSA and the best solution obtained is transferred to P&O algorithm in the later stage. The combined algorithm is shown to possess the principal advantages of the two methods resulting in improved tracking performance. Simulation and experimental studies on a prototype PV system show enhanced performance of the new method.

关键词： gravitational search algorithm MPPT Perturb and observe Photovoltaic systems

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gravitational search algorithm (GSA) Optimized Fuzzy-PID controller Design for Load frequency control of an interconnected multi-area power system

Gravitational Search Algorithm (GSA) Optimized Fuzzy-PID con...

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IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Debnath, Manoj Kumar Mallick, Ranjan Kumar Das, Sadhana Aman, Aditya Siksha O Anusandhan Univ Dept EE Bhubaneswar Odisha India Siksha O Anusandhan Univ Dept EEE Bhubaneswar Odisha India

ISBN: (纸本)9781509012770

This paper focuses on the load frequency control of two area interconnected reheat thermal systemwith fuzzy-PID controller optimized by gravitational search algorithm(GSA). The gains of the fuzzy-PID controllers are optimized by providing a step load perturbation(SLP) of 1% to area 1in consideration of integral time absolute error(ITAE) as the objective function. The dynamic performances of the system are being analyzed with GSA and PSO optimized fuzzy-PID controllers. The supremacy of the proposed method is depicted in terms of settling time, peak overshoots and undershoots in comparison with a recently published results. Further the robustness of the system is proved by changing the system loading.

关键词： Automatic Generation Control Particle Swarm Optimization gravitational search algorithm Fuzzy Logic Controller Proportional-Integral-Derivative controller

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gravitational search algorithm based Economic Load Dispatch Problem Incorporating Wind Generating System 1

Gravitational Search Algorithm based Economic Load Dispatch ...

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1st IEEE International Conference on Power Electronics, Intelligent Control, and Energy Systems (ICPEICES)

作者： Nath, Monalisa Mishra, Sanhita Nath, Desalin KIIT Univ Bhubaneswar Odisha India KIIT Univ BPUT Bhubaneswar Odisha India

ISBN: (纸本)9781467385879

Now economic load dispatch, generation of the power is always matched to meet prevailing load condition, so as maintain the constant frequency and stability of the network. Here the main goal of the economic load dispatch, the energy should supply to the consumer at minimum lowest possible cost. Hence, the cost of power delivered should be minimum for any load condition thus the economic load dispatch is played vital problems of power system operation and control. But in current proposal increase in fuel cost and environmental pollution, wind power is incorporated with the other generator for the fulfillment of the required power generation. The main aim of this work is to find optimal cost estimation in accordance with the power demand and wind availability by using gravitational search algorithm for the minimization of cost.

关键词： Economic Load Dispatch gravitational search algorithm Wind System Probability Density Function Cost Function

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Automatic Mining of Quantitative Association Rules with gravitational search algorithm

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2017年第3期27卷 343-372页

作者： Can, Umit Alatas, Bilal Munzur Univ Dept Comp Engn Tunceli Turkey Firat Univ Dept Software Engn Elazig Turkey

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with gravitational search algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

关键词： gravitational search algorithm data mining quantitative association rules

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Congestion management of deregulated power systems by optimal setting of Interline Power Flow Controller using gravitational search algorithm

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Journal of Electrical Systems and Information Technology 2017年第1期4卷 198-212页

作者： Akanksha Mishra Venkata Nagesh Kumar G. Department of EEE Vignan’s Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of EEE Vignan’s Institute of Information Technology Visakhapatnam Andhra Pradesh India

In a deregulated electricity market it may at times become difficult to dispatch all the required power that is scheduled to flow due to congestion in transmission lines. An Interline Power Flow Controller (IPFC) can be used to reduce the system loss and power flow in the heavily loaded line, improve stability and loadability of the system. This paper proposes a Disparity Line Utilization Factor for the optimal placement and gravitational search algorithm based optimal tuning of IPFC to control the congestion in transmission lines. DLUF ranks the transmission lines in terms of relative line congestion. The IPFC is accordingly placed in the most congested and the least congested line connected to the same bus. Optimal sizing of IPFC is carried using gravitational search algorithm. A multi-objective function has been chosen for tuning the parameters of the IPFC. The proposed method is implemented on an IEEE-30 bus test system. Graphical representations have been included in the paper showing reduction in LUF of the transmission lines after the placement of an IPFC. A reduction in active power and reactive power loss of the system by about 6% is observed after an optimally tuned IPFC has been included in the power system. The effectiveness of the proposed tuning method has also been shown in the paper through the reduction in the values of the objective functions.

关键词： IPFC Interline Power Flow Controller SSSC Static Synchronous Series Compensator TCSC thyristor controlled series compensator LUF Line Utilization Factor PSO particle swarm optimization GSA gravitational search algorithm VSC voltage source converter VD voltage deviation SM security margin Congestion Interline Power Flow Controller Line Utilization Factor IPFC placement IPFC tuning gravitational search algorithm

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A mixed-strategy based gravitational search algorithm for parameter identification of hydraulic turbine governing system

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KNOWLEDGE-BASED SYSTEMS 2016年第0期109卷 218-237页

作者： Zhang, Nan Li, Chaoshun Li, Ruhai Lai, Xijie Zhang, Yuanchuan. Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

A Mixed-Strategy based gravitational search algorithm (MS-GSA) is proposed in this paper, in which three improvement strategies are mixed and integrated in the standard GSA to enhance the optimization ability. The first improvement strategy is introducing elite agent's guidance into movement function to accelerate convergence speed. The second one is designing an adaptive gravitational constant function to keep a balance between the exploration and exploitation in the searching process. And the third improvement strategy is the mutation strategy based on the Cauchy and Gaussian mutations for overcoming the shortages of premature. The MS-GSA has been verified by comparing with 7 popular meta-heuristics algorithms on 23 typical basic benchmark functions and 7 CEC2005 composite benchmark functions. The results on these benchmark functions show that the MS-GSA has achieved significantly better performance than other algorithms. The effectiveness and significance of the results have been verified by Wilcoxon's test. Finally, the MS-GSA is employed to solve the parameter identification problem of Hydraulic turbine governing system (HTGS). It is shown that the MS-GSA is able to identify the parameters of HTGS effectively with higher accuracy compared with existing methods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： gravitational search algorithm Elite agent's guidance Adaptive gravitational constant function Hydraulic turbine governing system Parameter identification

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Feature Selection using gravitational search algorithm for Biomedical Data

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Procedia Computer Science 2017年 115卷 258-265页

作者： Sushama Nagpal Sanchit Arora Sangeeta Dey Shreya Netaji Subhas Institute of Technology Sector-3 Dwarka Delhi-110078 India

Analysis of medical data for disease prediction requires efficient feature selection techniques, as the data contains a large number of features. Researchers have used evolutionary computation (EC) techniques like genetic algorithms, particle swarm optimization etc. for FS and have found them to be faster than traditional techniques. We have explored a relatively new EC technique called gravitational search algorithm (GSA) for feature selection in medical datasets. This wrapper based method, that we have employed, using GSA and k-nearest neighbors reduces the number of features by an average of 66% and considerably improves the accuracy of prediction.

关键词： Feature Selection gravitational search algorithm Evolutionary Computation Curse of Dimensionality Biomedicine

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An improved gravitational search algorithm for green partner selection in virtual enterprises

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NEUROCOMPUTING 2016年 217卷 103-109页

作者： Xiao, Jianhua Niu, Yunyun Chen, Ping Leung, Stephen C. H. Xing, Fei Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China Nankai Univ Sch Business Tianjin 300071 Peoples R China Univ Hong Kong Fac Engn Hong Kong Hong Kong Peoples R China Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China

With the increasing public consciousness in environmental protection, the green partner selection problem (G-PSP) is an important issue in virtual enterprises. In this paper, the green criterion is introduced to partner selection problem (PSP), and a green partner selection model based on six criteria is proposed. As PSP has been proven to be an NP problem, and G-PSP cannot be solved in reasonable time by traditional methods. In this paper, an improved algorithm I-GSA/PSO that combines gravitational search algorithm and particle swarm optimization is developed to solve G-PSP in virtual enterprises. Experimental results show that I-GSA/PSO is effective and outperforms other evolutionary algorithms in solving G-PSP. (C) 2016 Published by Elsevier B.V.

关键词： gravitational search algorithm Particle swarm optimization Green partner selection Virtual enterprise

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A memory-based gravitational search algorithm for enhancing minimum variance distortionless response beamforming

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APPLIED SOFT COMPUTING 2016年第0期47卷 103-118页

作者： Darzi, Soodabeh Kiong, Tiong Sieh Islam, Mohammad Tariqul Soleymanpour, Hassan Rezai Kibria, Salehin Univ Kebangsaan Malaysia Ctr Space Sci Bangi 43600 Malaysia Univ Tenaga Nas Coll Engn Ctr Syst & Machine Intelligence Selangor Bandar Baru Ban Malaysia Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Malaysia Semnan Univ Dept Elect Engn Semnan Iran

This paper introduces a memory-based version of gravitational search algorithm (MBGSA) to improve the beamforming performance by preventing loss of optimal trajectory. The conventional gravitational search algorithm (GSA) is a memory-less heuristic optimization algorithm based on Newton's laws of gravitation. Therefore, the positions of agents only depend on the optimal solutions of previous iteration. In GSA, there is always a chance to lose optimal trajectory because of not utilizing the best solution from previous iterations of the optimization process. This drawback reduces the performance of GSA when dealing with complicated optimization problems. However, the MBGSA uses the overall best solution of the agents from previous iterations in the calculation of agents' positions. Consequently, the agents try to improve their positions by always searching around overall best solutions. The performance of the MBGSA is evaluated by solving fourteen standard benchmark optimization problems and the results are compared with GSA and modified GSA (MGSA). It is also applied to adaptive beamforming problems to improve the weight vectors computed by Minimum Variance Distortionless Response (MVDR) algorithm as a real world optimization problem. The proposed algorithm demonstrates high performance of convergence compared to GSA and Particle Swarm Optimization (PSO). (C) 2016 Elsevier B.V. All rights reserved.

关键词： gravitational search algorithm Minimum variance distortionless response Adaptive beamforming Particle Swarm Optimization Heuristic algorithm Artificial intelligence

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