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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 - 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 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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A Strength Pareto gravitational search algorithm for Multi-Objective Optimization Problems

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2015年第6期29卷 1559010-1559010页

作者： Yuan, Xiaohui Chen, Zhihuan Yuan, Yanbin Huang, Yuehua Zhang, Xiaopan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Wuhan Univ Technol Sch Resource & Environm Engn Wuhan 430070 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

A novel strength Pareto gravitational search algorithm (SPGSA) is proposed to solve multi-objective optimization problems. This SPGSA algorithm utilizes the strength Pareto concept to assign the fitness values for agents and uses a fine-grained elitism selection mechanism to keep the population diversity. Furthermore, the recombination operators are modeled in this approach to decrease the possibility of trapping in local optima. Experiments are conducted on a series of benchmark problems that are characterized by difficulties in local optimality, non-uniformity, and nonconvexity. The results show that the proposed SPGSA algorithm performs better in comparison with other related works. On the other hand, the effectiveness of two subtle means added to the GSA are verified, i.e. the fine-grained elitism selection and the use of SBX and PMO operators. Simulation results show that these measures not only improve the convergence ability of original GSA, but also preserve the population diversity adequately, which enables the SPGSA algorithm to have an excellent ability that keeps a desirable balance between the exploitation and exploration so as to accelerate the convergence speed to the true Pareto-optimal front.

关键词： gravitational search algorithm strength Pareto multi-objective optimization problems recombination operator fine-grained elitism selection

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Current studies and applications of Krill Herd and gravitational search algorithms in healthcare

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第SUPPL 1期56卷 1243-1277页

作者： Hamad, Rebwar Khalid Rashid, Tarik A. Erbil Polytech Univ Erbil Tech Engn Coll Dept Informat Syst Engn Erbil Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd algorithm (KH) and the gravitational search algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.

关键词： Healthcare Meta-huristic Nature inspired computing Krill Herd algorithm gravitational search algorithm

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Coverage Control algorithm for DSNs Based on Improved gravitational search

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IEEE SENSORS JOURNAL 2022年第7期22卷 7340-7351页

作者： Yao, Yindi Liao, Huanmin Li, Xiong Zhao, Feng Yang, Xuan Hu, Shanshan Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China Shaanxi Key Lab Informat Commun Network & Secur Xian Peoples R China Xian Univ Posts & Telecommun Sch Cyberspace Secur Xian 710121 Peoples R China

In directional sensor networks (DSNs), coverage control is an important way to ensure efficient communication and reliable data transmission. Aiming at enhancing the DSNs coverage performance, a coverage control algorithm based on improved gravitational search (IGSCCA) was proposed. Firstly, in the coverage enhancement phase, aiming at the defects of gravitational search algorithm that it is easy to fall into local optimum and slow convergence speed when solving the coverage enhancement problem, cosine decay function is used to improve the gravitational function, the global optimum position is introduced into the velocity updating of gravitational search algorithm, and use the uncovered grid points attractive force to disturb the worst particles. Secondly, in the node sleep phase, the node sleep strategy based on security set is introduced to reduce the network redundancy rate. Finally, the simulation results showed that this paper has great advantages in coverage rate, energy utilization rate and redundancy rate.

关键词： Sensors Force Robot sensing systems Redundancy Monitoring Wireless sensor networks Security Directional sensor networks (DSNs) gravitational search algorithm coverage rate redundancy rate

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sEMG Based Movement Quantitative Estimation of Joins Using SVM Method with gravitational search algorithm

sEMG Based Movement Quantitative Estimation of Joins Using S...

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第27届中国控制与决策会议

作者： Xingang Zhao Dongsheng Liu Dan Ye State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781479970186

Recently the surface electromyogram signal(sEMG) based motion estimation developed rapidly,which focus on intention recognition but the other information of motion is not *** paper proposed a sEMG based quantitative analysis method to estimate movement of human joints,which was used to control the upper limb rehabilitation robot system by participant's own *** quantitative model was established utilizing support vector machine(SVM).In order to improve the fitting accuracy and generalization ability of the support vector machine model,an algorithm for the SVM parameter optimization was proposed based on the gravitational search *** simulated experiments show that the SVM regression model based on the gravitational search algorithm has a high accuracy and strong generalization *** online experiments on rehabilitation robot controlled by a healthy participant demonstrated that the sEMG based control method using the proposed method was feasible.

关键词： sEMG motion estimation SVM gravitational search algorithm rehabilitation robot

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Optimal Energy Consumption Optimization in a Smart House by Considering Electric Vehicles and Demand Response via a Hybrid gravitational search and Particle Swarm Optimization algorithm

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Energy Engineering 2022年第6期119卷 2489-2511页

作者： Rongxin Zhang Chengying Yang Xuetao Li School of Economics and Management Hubei University of Automotive TechnologyShiyan442000China School of Management Universiti Sains MalaysiaPenang11800Malaysia School of Science Hubei University of Automotive TechnologyShiyan442002China

Buildings are the main energy consumers across the world,especially in urban *** smartization,or the smartification of housing,therefore,is a major step towards energy grid smartization *** controlling the energy consumption of lighting,heating,and cooling systems,energy consumption can be *** or some part of the energy consumed in future smart buildings must be supplied by renewable energy sources(RES),which mitigates environmental impacts and reduces peak demand for electrical *** this paper,a new optimization algorithm is applied to solve the optimal energy consumption problem by considering the electric vehicles and demand response in smart *** this way,large power stations that work with fossil fuels will no longer be *** current study modeled and evaluated the performance of a smart house in the presence of electric vehicles(EVs)with bidirectional power exchangeability with the power grid,an energy storage system(ESS),and solar ***,the solar RES and ESS for predicting solar-generated power prediction uncertainty have been considered in this *** case studies,including the sales of electrical energy resulting from PV panels’generated power to the power grid,time-variable loads such as washing machines,and different demand response(DR)strategies based on energy price variations were taken into account to assess the economic and technical effects of EVs,BESS,and solar *** proposed model was simulated in MATLAB.A hybrid particle swarm optimization(PSO)and gravitational search(GS)algorithm were utilized for *** generation and reduction were performed via LHS and backward methods,*** results demonstrate that the proposed model minimizes the energy supply cost by considering the stochastic time of use(STOU)loads,EV,ESS,and PV *** on the results,the proposed model markedly reduced the electricity costs of the smart house.

关键词： Energy management smart house particle swarm optimization algorithm gravitational search algorithm demand response electric vehicle

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Multi-objective optimization of foundation using global-local gravitational search algorithm

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STRUCTURAL ENGINEERING AND MECHANICS 2014年第3期50卷 257-273页

作者： Khajehzadeh, Mohammad Taha, Mohd Raihan Eslami, Mahdiyeh Islamic Azad Univ Anar Branch Dept Civil Engn Anar Iran Univ Kebangsaan Malaysia Dept Civil & Struct Engn Bangi 43600 Selangor Malaysia Islamic Azad Univ Dept Elect Engn Sci & Res Branch Kerman Iran

This paper introduces a novel optimization technique based on gravitational search algorithm (GSA) for numerical optimization and multi-objective optimization of foundation. In the proposed method, a chaotic time varying system is applied into the position updating equation to increase the global exploration ability and accurate local exploitation of the original algorithm. The new algorithm called global-local GSA (GLGSA) is applied for optimization of some well-known mathematical benchmark functions as well as two design examples of spread foundation. In the foundation optimization, two objective functions include total cost and CO2 emissions of the foundation subjected to geotechnical and structural requirements are considered. From environmental point of view, minimization of embedded CO2 emissions that quantifies the total amount of carbon dioxide emissions resulting from the use of materials seems necessary to include in the design criteria. The experimental results demonstrate that, the proposed GLGSA remarkably improves the accuracy, stability and efficiency of the original algorithm.

关键词： spread foundation cost optimization CO2 emissions optimization gravitational search algorithm

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Multi-objective optimisation of retaining walls using hybrid adaptive gravitational search algorithm

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CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS 2014年第3期31卷 229-242页

作者： Khajehzadeh, Mohammad Taha, Mohd Raihan Eslami, Mahdiyeh Islamic Azad Univ Anar Branch Civil Engn Dept Anar Iran Natl Univ Malaysia Civil & Struct Engn Dept Bangi Selangor Malaysia Islamic Azad Univ Sci & Res Branch Elect Engn Dept Kerman Iran

This paper presents an effective hybrid evolutionary approach for multi-objective optimisation of reinforced concrete (RC) retainingwalls. The proposed algorithm combines an adaptive gravitational search algorithm (AGSA) with pattern search (PS) called AGSA-PS. In the resulting hybrid approach, the PS algorithm is employed as a local search algorithm around the global solution found by AGSA. The proposed algorithm was tested on a set of five well-known benchmark functions and simulation results demonstrate the superiority of the new method compared with the standard algorithm. Thereafter, the proposed AGSA-PS is applied for multi-objective optimisation of RC retaining walls. Two objective functions include total cost and embedded CO2 emissions of retaining wall are considered. The reliability and efficiency of the AGSA-PS for multi-objective optimisation of retaining structures are investigated by considering two design examples of retaining walls. Experimental results demonstrate that the resulting algorithm has high viability, accuracy and significantly outperforms the original algorithm and some other methods in the literature.

关键词： retaining wall multi-objective optimisation gravitational search algorithm pattern search

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