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检索条件"主题词=Gravitational Search algorithm"

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gravitational search algorithm For the Static Synchronous Series Compensator Based Damping Controller Design

Gravitational Search Algorithm For the Static Synchronous Se...

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3rd IEEE Students' Technology Symposium (IEEE TechSym)

作者： Khadanga, Rajendra Ku. Satapathy, Jitendriya Ku. Natl Inst Technol Dept Elect Engn Rourkela Orissa India

ISBN: (纸本)9781479926084

This paper demonstrated an efficient approach for designing a static synchronous series compensator (SSSC) based supplementary controllers for damping low frequency oscillations. The SSSC is mainly GTO based which can control different parameters of power system. A supplementary damping controller for a SSSC is designed for power system dynamic performance enhancement. The lead-lag structure of damping controller is used for the damping of power system oscillation. The proposed controllers design problem is formulated as an optimization problem. The gravitational search algorithm (GSA) is employed to optimize the damping controller parameters. To demonstrate the effectiveness of the proposed adaptive SSSC based supplementary damping controller, computer simulations are performed on a power system considering a single machine infinite bus installed with SSSC. The Simulation results are presented and these results are compared with a conventional method of tuning the damping controller parameters. This will analyze the effectiveness and robustness of the proposed design approach.

关键词： power system oscillations gravitational search algorithm flexible AC transmission systems static synchronous series compensator (SSSC) damping controller

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gravitational search algorithm in Recommendation Systems 8th

Gravitational Search Algorithm in Recommendation Systems

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8th International Conference on Swarm Intelligence (ICSI)

作者： Choudhary, Vedant Mullick, Dhruv Nagpal, Sushama Netaji Subhas Inst Technol Sect 3 Delhi 110078 India

ISBN: (纸本)9783319618333;9783319618326

Recommendation Systems have found extensive use in today's web environment as they improve the overall user experience by providing users with personalized suggestions. Along with the traditional techniques like Collaborative and Content-based filtering, researchers have explored computational intelligence techniques to improve the performance of recommendation systems. In this paper, a similar approach has been taken in the form of applying a heuristic based technique on recommendation systems. The paper proposes a recommendation system based on a less explored nature-inspired technique called gravitational search algorithm. The performance of this system is compared with that of a system using Particle Swarm Optimisation, which is a similar optimisation technique. The results show that gravitational search algorithm excels in improving the accuracy of the recommendation model and also surpasses the model using Particle Swarm Optimization.

关键词： Recommendation systems Computational intelligence gravitational search algorithm Particle Swarm Optimisation Collaborative filtering

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gravitational search algorithm - based Optimization of process parameters in Micro turning process

Gravitational Search Algorithm - based Optimization of proce...

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16th International Mechanical Engineering Congress (IMEC)

作者： Durairaj, M. Gowri, S. Tagore Engn Coll Dept Mech Engn Madras 127 Tamil Nadu India Anna Univ EMMRC Chennai 600025 Tamil Nadu India

ISBN: (纸本)9783038351634

Micro turning is a scaled down version of conventional turning process, but operating on the micro scale of machining parameters to produce micro components. This paper deals with CNC Micro turning of Inconel 600 alloy with titanium carbide coated tool. Two conflicting objectives, surface roughness and tool flank wear, are simultaneously optimized. Full factorial experiments were taken with several combinations of cutting speed, feed and depth of cut. In this report, a new optimization algorithm based on the law of gravitation and mass interactions, namely gravitational search algorithm (GSA) is aimed to predict the optimal parameter conditions for controlling tool flank wear and better surface finish.

关键词： Micro turning gravitational search algorithm optimisation surface roughness flank wear

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gravitational search algorithm with a new technique 13

Gravitational Search Algorithm with a new technique

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13th International Conference on Computational Intelligence and Security (CIS)

作者： Li, Juan Dong, Ning Shaanxi Normal Univ Sch Distance Educ Xian Shaanxi Peoples R China Shaanxi Normal Univ Sch Math & Info Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9781538648223

gravitational search algorithm (GSA) has been invented by Newton gravitational Law to solve complex global optimization problem. Many improved GSA has been proposed by many researches to improve its performance. In this paper, a new updating mechanism has been proposed to prevent premature convergence and stagnation in evolution. The core idea of particle swarm optimization (PSO) is introduced into GSA to overcome these issues. Some nonlinear benchmark functions are chosen to verify the effectiveness of new algorithm. The numerical experimental results show that the new algorithm is robustness.

关键词： gravitational search algorithm global optimization particle swarm optimization

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gravitational search algorithm with Heuristic search for Clustering Problems

Gravitational Search Algorithm with Heuristic Search for Clu...

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3rd Conference on Data Mining and Optimization (DMO)/1st Multi Conference on Artificial Intelligence Technology (MCAIT)

作者： Hatamlou, Abdolreza Abdullah, Salwani Othman, Zalinda Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimisat Res Grp DMO Bangi 43600 Selangor Malaysia

ISBN: (纸本)9781612842127

In this paper, we present an efficient algorithm for cluster analysis, which is based on gravitational search and a heuristic search algorithm. In the proposed algorithm, called GSA-HS, the gravitational search algorithm is used to find a near optimal solution for clustering problem, and then at the next step a heuristic search algorithm is applied to improve the initial solution by searching around it. Four benchmark datasets are used to evaluate and to compare the performance of the presented algorithm with two other famous clustering algorithms, i.e. K-means and particle swarm optimization algorithm. The results show that the proposed algorithm can find high quality clusters in all the tested datasets.

关键词： Cluster analysis gravitational search algorithm Heuristic search

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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 using CUDA 15

Gravitational Search Algorithm using CUDA

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15th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Zarrabi, Amirreza Karuppiah, Ettikan K. Kok, Yong Keh Hai, Ngo Chuan See, Simon MIMOS Berhad Technol Pk Malaysia Kuala Lumpur 5700 Malaysia NVIDIA Corp Santa Clara CA USA

ISBN: (纸本)9781479983346

Many scientific and technical problems with massive computation requirements could benefit from the Graphics Processing Units (GPUs) using Compute Unified Device Architecture (CUDA) for high speed processing. gravitational search algorithm (GSA) is a population-based metaheuristic algorithm that can be effectively implemented on GPU to reduce the execution time. In this paper we discuss possible approaches to parallelize GSA on graphics hardware using CUDA. An in-depth study of the computation efficiency of parallel algorithms and capability to effectively exploit the architecture of GPU is performed. Additionally, a comparative study of parallel and sequential GSA was carried out on a set of standard benchmark optimization functions. The results show a significant speedup that re-emphasizes the utility of CUDA based implementation for complex and computationally intensive parallel applications.

关键词： gravitational search algorithm Graphic Processing Units CUDA

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gravitational search algorithm for Estimating the Kinetic Parameters of the Propane Pre-reforming Reaction 6

Gravitational Search Algorithm for Estimating the Kinetic Pa...

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6th International Conference on Information Technology and Nanotechnology (IEEE ITNT)

作者： Enikeeva, Leniza Enikeev, Marat Gubaydullin, Trek Shamshovich, Valentina Novosibirsk State Univ Novosibirsk Russia Ufa State Petr Technol Univ Ufa Russia Russian Acad Sci Inst Petrochem & Catalysis Moscow Russia

ISBN: (纸本)9781728170411

The article describes a gravitational search algorithm and its application to solving the inverse problem of chemical kinetics. The relevance of the study of metaheuristic algorithms, including the gravitational search algorithm, is given. It is shown that recently, these algorithms are becoming increasingly popular. The optimization problem is formulated on the example of solving the inverse kinetic problem. The process under study is propane pre-reforming into methane-rich gas over Ni catalyst, which is an industrially important chemical process. The description of the algorithm and its pseudo-code are presented, after which the performance of the gravitational search algorithm is compared with other metaheuristic methods. The algorithm demonstrated its competitiveness, as a result of which it was applied to solve a specific industrial problem. Using this algorithm, the inverse problem of chemical kinetics was solved, and the optimal values of the kinetic parameters of the reaction were found. It was proved that the model correctly describes the available experimental data.

关键词： mathematical modeling chemical kinetics gravitational search algorithm

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gravitational search algorithm Combined with Modified Differential Evolution Learning for Planarization in Graph Drawing 5

Gravitational Search Algorithm Combined with Modified Differ...

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2017 International Conference on Progress in Informatics and Computing(PIC)

作者： Hang Yu Huisheng Zhu Huiqin Chen Dongbao Jia Yang Yu Shangce Gao College of Computer Science and Technology Taizhou University Department of Agricultural Internet of Things Jiangsu Agri-animal Husbandry Vocational College Faculty of Engineering University of Toyama

ISBN: (纸本)9781538619797;9781538619780

gravitational search algorithm(GSA) is one of the powerful population based meta-heuristics. It has achieved many successes in various applications derived from optimization, data mining, information security, etc. However, it still suffers from the local optima trapping problem and cannot obtain promising solutions especially for practical problems. Graph planarization arises from many practical applications of VLSI circuit design,automatic graph drawing, etc, and is proved to be *** solve this problem, this study proposes a hybrid GSA by combined with a differential evolution operator. The proposed method GSADE is used to acquire optimal planar subgraphs for a given graph. Experimental results based on thirty graph instances show that GSADE is a very competitive method in comparison with previous state-of-the-art methods.

关键词： gravitational search algorithm differential evolution artificial intelligence VLSI design graph planarization

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gravitational search algorithm Optimized State Feedback Load Frequency Controller 15

Gravitational Search Algorithm Optimized State Feedback Load...

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15th IEEE-India-Council International Conference (IEEE INDICON)

作者： Ghosh, Arabinda Singh, Omkar Ray, Anjan Kumar Nurujjaman, Md Natl Inst Technol Sikkim Elect & Elect Engn Dept Ravangla Sikkim India Natl Inst Technol Sikkim Phys Dept Ravangla Sikkim India

ISBN: (纸本)9781538682357

To maintain reliable and stable operation in a power system, maintaining the value of frequency variation within a desired limit is important. In this paper, a state feedback controller is designed for a load frequency control (LFC) system. This process of defining each state will help the operator to understand the cumulative behavior of different states on the system performance in presence of external disturbances. A generalized range for stability is derived depending on the physical parameters of the system. Instead of manually selecting the controller gain values from stable range, an optimization algorithm is used. In this paper, gravitational search algorithm (GSA) is used as an optimization algorithm. GSA minimizes the effect of external disturbance on system frequency and gives a set of controller values satisfying system stability criterion. A set of results is presented to show the improved closed loop performance of the system. A comparative study with the previous work of the authors and with a few existing works is also presented to highlight the advantages of the proposed method.

关键词： Load Frequency Control State feedback Controller System Stability gravitational search algorithm PID

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