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

作者： Kumar, Dev Soni, Bhanu Pratap Ray, Soumyadeep Gupta, Vikas Malaviya Natl Inst Technol Jaipur Dept Elect Engn Jaipur Rajasthan India

ISBN: (纸本)9781467385879

This paper presents the effect of different controllers on Load Frequency Control (LFC) of a two area interconnected thermal power system for the advancement of dynamic performance. Time domain simulations are performed to investigate the performance of the system in the presence of generation rate constraint (GRC). The Controller consists of I, PI and PID compensator where the gain setting of proportional (P), integral (I) and derivative (D) constants are optimizes using gravitational search algorithm (GSA) and the objective function used for minimization is Integral of Time multiplied with Absolute value of Error (IT AE). Results reveal that for a step load disturbance, PID controller gives the better dynamic performance of LFC on I and PI compensator and keep the frequency and tie-line power within the range;hence, it proves the effectiveness of GSA based PID compensator in two area interconnected thermal system.

关键词： Load Frequency Control gravitational search algorithm PID Controller Generation Rate Constraint ACE Participation Factor

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gravitational search algorithm for synthesis of selectively thinned concentric ring array antenna with minimum sidelobe level and with fixed and variable first null beamwidth

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INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES 2015年第6期7卷 775-781页

作者： Chatterjee, Anirban Mahanti, Gautam Kumar Pathak, Narendra Nath Natl Inst Technol Goa Dept Elect & Commun Engn Farmagudi Ponda India Natl Inst Technol Durgapur Dept Elect & Commun Engn Durgapur India Dr BC Roy Engn Coll Durgapur Dept Elect & Commun Engn Durgapur India

Thinning a large concentric ring array by an evolutionary algorithm needs to handle a large amount of variables. The computational time to find out the optimum elements set increases with the increase of array size. Moreover, thinning significantly reduces the directivity of the array. In this paper, the authors propose a pattern synthesis method to reduce the peak sidelobe level (peak SLL) while keeping first null beamwidth (FNBW) of the array fixed by thinning the outermost rings of the array based on gravitational search algorithm (GSA). Two different cases have been studied. In the first case only the outermost ring of the array is thinned and in the second case the two outermost rings are thinned. The FNBW of the optimized array is kept equal to or less than that of a fully populated, uniformly excited and 0.5 spaced concentric ring array of same number of elements and rings. The directivity of the optimized array for the above two cases are compared with an array optimized by thinning all the rings, while keeping the design criteria same as the above two cases. The optimized array by thinning the outermost rings gives higher directivity over the optimized array by thinning all the rings. Time required for computing the optimum elements state for the above two cases using GSA are shown lesser compared to the optimized array by thinning all the rings using the same algorithm. The peak SLL and the FNBW of the optimized array for the above two cases are also compared with the optimized array by thinning all the rings.

关键词： Concentric ring array Directivity First null beamwidth gravitational search algorithm Peak SLL Thinning

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Fuzzy entropy based multilevel image thresholding using modified gravitational search algorithm

Fuzzy entropy based multilevel image thresholding using modi...

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IEEE International Conference on Industrial Technology (ICET)

作者： Chao, Yuan Dai, Min Chen, Kai Chen, Ping Zhang, Zhisheng Southeast Univ Sch Mech Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781467380751

Multilevel image thresholding is a powerful and commonly used technique in image analysis. Conventional image segmentation methods suffer a large amount of computation time and unstable segmentation results. In this paper, we present a multilevel image thresholding method based on fuzzy entropy and modified gravitational search algorithm. Fuzzy entropy based image thresholding is extended to multilevel, and a modified gravitational search algorithm (mGSA) is proposed to accelerate the multilevel fuzzy entropy maximization process. Experimental results show that the proposed method can obtain optimal multilevel thresholds and the proposed mGSA has more accurate and stable segmentation results compared with firefly algorithm (FA) and cuckoo search (CS).

关键词： multilevel thresholding image segmentation fuzzy entropy gravitational search algorithm

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A new improved gravitational search algorithm based on chaos 12

A new improved gravitational search algorithm based on chaos

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12th International Conference on Computational Intelligence and Security (CIS)

作者： Ding, Wang Xian Univ Sci & Technol Coll Sci Xian Peoples R China

ISBN: (纸本)9781509048403

For the past few years, many swarm intelligent optimization methods have been proposed. In this article, a new optimization algorithm based on gravitational search algorithm and chaos is introduced. In the new algorithm, chaotic operator is used by population initialization. Population would be regenerated when premature of algorithm. The new algorithm has been compared with some optimization methods. Numerical results confirm the high performance of the constructed method in solving global optimization problems.

关键词： gravitational search algorithm chaos global optimization benchmark function

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A Feature Selection Method Based on Multi-objective Optimisation with gravitational search algorithm 3rd

A Feature Selection Method Based on Multi-objective Optimisa...

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3rd Annual International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem (GRMSE)

作者： Dickson, Bolou Bolou Wang, Shengsheng Dong, Ruyi Wen, Changji Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9783662491553;9783662491546

The process of feature selection (FS) is a substantial task that has a significant effect in the performance of a given algorithm. The goal is to choose a subset of available features by eliminating the unnecessary features. This hybrid algorithm is in maximising the classification performance and minimising the number of features to achieve an outstanding performance through a less complex procedure. From the experiments, FSMOGSA was noted to be quite unparalleled in comparison with other methods in reducing the error rate, and maximising the general performance through irrelevant feature reduction.

关键词： gravitational search algorithm Indexed non-dominated solutions Pareto front Multi-objective optimisation Feature selection

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Accelerative gravitational search algorithm 5

Accelerative Gravitational Search Algorithm

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Gupta, Aditi Sharma, Nirmala Sharma, Harish Rajasthan Tech Univ Kota India

ISBN: (纸本)9781509020287

gravitational search algorithm (GSA) is a meta-heuristic search algorithm based on the Newton's gravity law. In this article, a new variant of GSA is introduced, namely Accelerative gravitational search algorithm (AGSA). In the proposed AGSA, an acceleration coefficient is introduced in the velocity update equation to control the acceleration of the individual. In the proposed position update process individuals are allowed to explore the search space in early iterations while exploitation in later iterations. The reliability, robustness and accuracy of the proposed algorithm is measured through various statistical analyses over 12 complex test problems. To show the competitiveness of the proposed strategy, the reported results are compared with the results of GSA, Biogeography Based Optimization (BBO), and Differential Evaluation (DE) algorithms.

关键词： gravitational search algorithm Swarm Intelligence Heuristic search algorithm Acceleration Law of Gravity

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A fuzzy system for dynamic parameter adaptation in gravitational search algorithm 8

A fuzzy system for dynamic parameter adaptation in gravitati...

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8th IEEE International Conference on Intelligent Systems (IS)

作者： Olivas, Frumen Valdez, Fevrier Castillo, Oscar Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781509013531

An improved methodology for parameter adaptation in GSA (gravitational search algorithm) is presented in where we use a fuzzy system to control the abilities of GSA to perform a global and local search. The results show that with our methodology GSA can outperform the quality of the results when compared with the original method and some other GSA improvements.

关键词： GSA fuzzy logic gravitational search algorithm dynamic parameter adaptation

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Hybridization of gravitational search algorithm and Biogeography Based Optimization and its application on Grid Scheduling problem 9

Hybridization of Gravitational Search Algorithm and Biogeogr...

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9th International Conference on Contemporary Computing (IC3)

作者： Goel, Lavika Singhal, Sunita Mishra, Sharthak Mohanty, Satyajit BITS Pilani Dept Comp Sci & Informat Syst Pilani Campus Pilani Rajasthan India

ISBN: (纸本)9781509032518

The gravitational search algorithm (GSA) is a nature inspired optimization algorithm which is based on Newton's law of gravity and law of motion. Biogeography Based Optimization (BBO) is also another nature inspired optimization algorithm based on the concept of biogeography (migration and mutation among population). Both of these optimization technique are population based and individually have been applied to a large number of areas. In this paper, we are providing a hybrid GSABBO algorithm that will use the best properties of both the algorithm to enhance the exploration and exploitation properties and reach at the global optimal solution. Grid Computing refers to the sharing of resources across multiple domains to achieve a common goal. Sharing of the resources within an organization helps to enhance its overall performance computationally and economically. The advantages derived from Grid Computing are largely dependent on the scheduling algorithm we use to schedule various jobs across various resources available. This paper introduces a new approach based on the hybridization of BBO and GSA to generate optimal schedules to complete all the given tasks with minimum make span period.

关键词： Biogeography Based Optimization gravitational search algorithm Hybridization Grid Computing Scheduling

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gravitational search algorithm Based Design of Optimal Wideband IIR Digital Differentiator 12

Gravitational Search Algorithm Based Design of Optimal Wideb...

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12 IEEE Int C Elect Energy Env Communications Computer Control

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

ISBN: (纸本)9781467365406

This paper aims to design IIR digital differentiator by adopting a new heuristic algorithm called a gravitational search algorithm (GSA). In GSA, agents are treated as the masses and the force of attraction between agents is calculated based on Newton's gravitation law to find optimal solutions to the problem. The agent with greater mass is treated as the near optimal solution and their position is a reflection of the solution. Simulations to find optimal filter coefficients of second, third and fourth order differentiators using GSA have been performed. Total absolute magnitude error and maximum phase error are the performance measures that are used to evaluate performance of the proposed differentiators. The result of the proposed GSA based approach has been compared with the standard existing algorithms like PSO, GA, SA, pole zero (PZ) and segment rule. Simulations show that GSA gives superior results when compared with the optimization abilities of other standard algorithms.

关键词： Digital Differentiator Infinite Impulse Response Heuristic algorithm gravitational search algorithm

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Multi-robot path planning in a dynamic environment using improved gravitational search algorithm

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Journal of Electrical Systems and Information Technology 2016年第2期3卷 295-313页

作者： P.K. Das H.S. Behera P.K. Jena B.K. Panigrahi Dept. of Comp. Sc. and Engineering and Information Technology VSSUT Burla Odisha India Dept. of Mechanical Engineering VSSUT Burla Odisha India Dept. of Electrical Engineering IIT Delhi India

This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm (IGSA) in a dynamic environment. GSA is improved based on memory information, social, cognitive factor of PSO (particle swarm optimization) and then, population for next generation is decided by the greedy strategy. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position. Finally, the analytical and experimental results of the multi-robot path planning have been compared with those obtained by IGSA, GSA and PSO in a similar environment. The simulation and the Khepera environmental results outperform IGSA as compared to GSA and PSO with respect to performance matrix.

关键词： gravitational search algorithm Multi-robot path planning Average total trajectory path deviation Average uncovered trajectory target distance Average path length

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