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Convergence analysis and performance of an improved gravitational search algorithm

APPLIED SOFT COMPUTING 2014年第0期24卷 363-384页

作者： Jiang, Shanhe Wang, Yan Ji, Zhicheng Jiangnan Univ Inst Elect Automat Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China Anqing Normal Coll Dept Phys & Power Engn Anqing 246011 Peoples R China

gravitational search algorithm (GSA) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence and loses the abilities of exploration and exploitation when solving complex problems. This paper presents an improved gravitational search algorithm (IGSA) that first employs chaotic perturbation operator and then considers memory strategy to overcome the aforementioned problems. The chaotic operator can enhance its global convergence to escape from local optima, and the memory strategy provides a faster convergence and shares individual's best fitness history to improve the exploitation ability. After that, convergence analysis of the proposed IGSA is presented based on discrete-time linear system theory and results show that IGSA is not only guaranteed to converge under the conditions, but can converge to the global optima with the probability 1. Finally, choice of reasonable parameters for IGSA is discussed on four typical benchmark test functions based on sensitivity analysis. Moreover, IGSA is tested against a suite of benchmark functions with excellent results and is compared to GA, PSO, HS, WDO, CFO, APO and other well- known GSA variants presented in the literatures. The results obtained show that IGSA converges faster than GSA and other heuristic algorithms investigated in this paper with higher global optimization performance. (C) 2014 Elsevier B.V. All rights reserved.

关键词： gravitational search algorithm Memory strategy Chaotic perturbation operator Convergence analysis Parameter sensitivity Performance evaluation

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A discrete gravitational search algorithm for solving combinatorial optimization problems

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INFORMATION SCIENCES 2014年 258卷 94-107页

作者： Dowlatshahi, Mohammad Bagher Nezamabadi-Pour, Hossein Mashinchi, Mashaallah Shahid Bahonar Univ Kerman Dept Comp Sci Kerman Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Shahid Bahonar Univ Kerman Dept Stat Kerman Iran

Metaheuristics are general search strategies that, at the exploitation stage, intensively exploit areas of the solution space with high quality solutions and, at the exploration stage, move to unexplored areas of the solution space when necessary. The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic that was originally designed for solving continuous optimization problems. It has a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. In this paper, a Discrete gravitational search algorithm (DGSA) is proposed to solve combinatorial optimization problems. The proposed DGSA uses a Path Re-linking (PR) strategy instead of the classic way in which the agents of GSA usually move from their current position to the position of other agents. The proposed algorithm was tested on a set of 54 Euclidean benchmark instances of TSP with sizes ranging from 51 to 2392 nodes. The results were satisfactory and in the majority of the instances, the results were equal to the best known solution. The proposed algorithm ranked ninth when compared with 54 different algorithms with regard to quality of the solution. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Metaheuristics gravitational search algorithm Combinatorial optimization Traveling salesman problem Path re-linking

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An Improved gravitational search algorithm for Dynamic Neural Network Identification

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International Journal of Automation and computing 2014年第4期11卷 434-440页

作者： Bao-Chang Xu Ying-Ying Zhang Department of Automation China University of Petroleum (Beijing)

gravitational search algorithm(GSA) is a newly developed and promising algorithm based on the law of gravity and interaction between masses. This paper proposes an improved gravitational search algorithm(IGSA) to improve the performance of the GSA, and first applies it to the field of dynamic neural network identification. The IGSA uses trial-and-error method to update the optimal agent during the whole search process. And in the late period of the search, it changes the orbit of the poor agent and searches the optimal agent s position further using the coordinate descent method. For the experimental verification of the proposed algorithm,both GSA and IGSA are testified on a suite of four well-known benchmark functions and their complexities are compared. It is shown that IGSA has much better efficiency, optimization precision, convergence rate and robustness than GSA. Thereafter, the IGSA is applied to the nonlinear autoregressive exogenous(NARX) recurrent neural network identification for a magnetic levitation *** with the system identification based on gravitational search algorithm neural network(GSANN) and other conventional methods like BPNN and GANN, the proposed algorithm shows the best performance.

关键词： gravitational search algorithm orbital change optimization neural network system identification

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Improved gravitational search algorithm for parameter identification of water turbine regulation system

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ENERGY CONVERSION AND MANAGEMENT 2014年 78卷 306-315页

作者： Chen, Zhihuan Yuan, Xiaohui Tian, Hao Ji, Bin Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

Parameter identification of water turbine regulation system (WTRS) is crucial in precise modeling hydropower generating unit (HGU) and provides support for the adaptive control and stability analysis of power system. In this paper, an improved gravitational search algorithm (IGSA) is proposed and applied to solve the identification problem for WTRS system under load and no-load running conditions. This newly algorithm which is based on standard gravitational search algorithm (GSA) accelerates convergence speed with combination of the search strategy of particle swarm optimization and elastic-ball method. Chaotic mutation which is devised to stepping out the local optimal with a certain probability is also added into the algorithm to avoid premature. Furthermore, a new kind of model associated to the engineering practices is built and analyzed in the simulation tests. An illustrative example for parameter identification of WTRS is used to verify the feasibility and effectiveness of the proposed IGSA, as compared with standard GSA and particle swarm optimization in terms of parameter identification accuracy and convergence speed. The simulation results show that IGSA performs best for all identification indicators. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Water turbine regulation system Parameter identification gravitational search algorithm Particle swarm optimization Chaotic mutation

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A gravitational search algorithm for multimodal optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2014年第0期14卷 1-14页

作者： Yazdani, Sajjad Nezamabadi-pour, Hossein Kamyab, Shima Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

gravitational search algorithm (GSA) has been recently presented as a new heuristic search algorithm with good results in real-valued and binary encoded optimization problems which is categorized in swarm intelligence optimization techniques. The aim of this article is to show that GSA is able to find multiple solutions in multimodal problems. Therefore, in this study, a new technique, namely Niche GSA (NGSA) is introduced for multimodal optimization. NGSA extends the idea of partitioning the main population (swarm) of masses into smaller sub-swarms and also preserving them by introducing three strategies: a K-nearest neighbors (K-NN) strategy, an elitism strategy and modification of active gravitational mass formulation. To evaluate the performance of the proposed algorithm several experiments are performed. The results are compared with those of state-of-the-art niching algorithms. The experimental results confirm the efficiency and effectiveness of the NGSA in finding multiple optima on the set of unconstrained and constrained standard benchmark functions. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Multimodal optimization Niching methods gravitational search algorithm Heuristic algorithms

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Task scheduling on computational Grids using gravitational search algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2014年第3期17卷 1001-1011页

作者： Zarrabi, Amirreza Samsudin, Khairulmizam Univ Putra Malaysia Dept Comp & Commun Syst Engn Fac Engn Upm Serdang 43400 Selangor Malaysia

Grid computing uses distributed interconnected computers and resources collectively to achieve higher performance computing and resource sharing. Task scheduling is one of the core steps to efficiently exploit the capabilities of Grid environment. Recently, heuristic algorithms have been successfully applied to solve task scheduling on computational Grids. In this paper, gravitational search algorithm (GSA), as one of the latest population-based metaheuristic algorithms, is used for task scheduling on computational Grids. The proposed method employs GSA to find the best solution with the minimum makespan and flowtime. We evaluate this approach with Genetic algorithm (GA) and Particle Swarm Optimization (PSO) method. The results demonstrate that the benefit of the GSA is its speed of convergence and the capability to obtain feasible schedules.

关键词： Grid task scheduling gravitational search algorithm Makespan Flowtime

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Optimal power flow for distribution networks using gravitational search algorithm

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ELECTRICAL ENGINEERING 2014年第4期96卷 335-345页

作者： Radosavljevic, Jordan Jevtic, Miroljub Arsic, Nebojsa Klimenta, Dardan Univ Pristina Kosovska Mitrov Fac Tech Sci Kosovska Mitrovica 38220 Serbia

This paper presents a gravitational search algorithm (GSA)-based approach to solve the optimal power flow (OPF) problem in a distribution network with distributed generation (DG) units. The OPF problem is formulated as a nonlinear optimization problem with equality and inequality constraints, where optimal control settings in case of fuel cost minimization of DG units, power loss minimization in the distribution network, and finally simultaneous minimization of the fuel cost and power loss are obtained. The proposed approach is tested on an 11-node test system and on a modified IEEE 34-node test system. Simulation results obtained from the proposed GSA approach are compared with that obtained using a genetic algorithm approach. The results show the effectiveness and robustness of the proposed GSA approach.

关键词： Optimal power flow Distributed generation Distribution network gravitational search algorithm

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Enhancing forest optimization algorithm with gravitational search for nonlinear continuous optimization

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INTERNATIONAL JOURNAL OF GENERAL SYSTEMS 2024年第7-8期53卷 971-1013页

作者： Farzi-Veijouyeh, Najibeh Matin, Neda Sahargahi, Vahideh Univ Tabriz Fac Elect & Comp Engn Dept Comp Engn Tabriz Iran Islamic Azad Univ Dept Comp Engn Shabestar Branch Shabestar Iran

Meta-heuristic algorithms have great role in solving problems related to optimization. Meta-heuristic method cannot solve problems related to optimization due to No Free Lunch theory. Hence different optimization methods are proposed by various researchers each year in order to solve optimization problems. Forest Optimization algorithm (FOA) is an evolutionary optimization algorithm that is appropriate for continuous nonlinear optimization problems. The algorithm drawbacks include entrapment in local optimum and failure in achieving global optimum. The paper proposes hybrid algorithm called FOAGSA, in which the gravitational search algorithm (GSA) is employed to improve the FOA performance in order to solve nonlinear continuous problems. The FOAGSA was evaluated through 39 benchmark optimization functions and two engineering problems. The experimental results proved that the FOAGSA exhibited acceptable results compared to state-of-art and well-known Meta-heuristic algorithms. Friedman ranking algorithm was utilized to compare FOAGSA with existing methods. The FOAGSA was ranked first on that basis.

关键词： Optimization metaheuristic algorithms Forest Optimization algorithm gravitational search algorithm hybrid algorithm

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Optimal Design of Power System Stabilizer Using Oppositional gravitational search algorithm

Optimal Design of Power System Stabilizer Using Oppositional...

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1st International Conference on Non Conventional Energy (ICONCE)

作者： Paul, S. Roy, P. K. Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India

ISBN: (纸本)9781479933396

In this paper, oppositional based gravitational search algorithm (OGSA) is presented to tune the parameters of power system stabilizer which are added to the excitation to damp low frequency oscillation that pertain in large power system. The most important issue for applying oppositional based GSA is to reach the optimal value in less time. This scheme enables the process to reach the desired value in smaller search space. Computation results illustrate that the proposed techniques is more effective in improving the dynamic performance by damping the low frequency oscillations. The performance of the proposed algorithm is weighed up for different loading conditions. Also the proposed algorithm is more effective in attaining the dynamic stability of the system when compared with other population based optimization techniques like gravitational algorithm (GSA) and differential evolution (DE).

关键词： Power system stabilizer Single machine infinite bus Damping ratio Eigen value gravitational search algorithm Opposition based learning

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OPTIMIZATION OF CUTTING PARAMETERS IN WIRECUT EDM OF D2 DIE STEEL USING gravitational search algorithm

OPTIMIZATION OF CUTTING PARAMETERS IN WIRECUT EDM OF D2 DIE ...

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16th International Mechanical Engineering Congress (IMEC)

作者： Durairaj, M. Elanthirayan, S. Aanand, K. Poornachandran, J. Tagore Engn Coll Dept Mech Engn Madras 127 Tamil Nadu India

ISBN: (纸本)9783038351634

Wire cut Electrical Discharge Machining is one of the important manufacturing process which is used to obtain desired shape using electrical discharge (or) by continuous sparking. This paper deals with wire cut EDM of D2 die steel using Zinc coated wire tool. Two conflicting objectives, surface roughness and kerf width, are simultaneously optimized. Experimentation was planned based on Taguchi's L-9 orthogonal array. All the experiments has been conducted under different machining conditions of gap voltage, pulse ON time, and pulse OFF time. Wire feed, wire speed, resistance, wire tension, dielectric fluid pressure and cutting length are taken as fixed parameters. In this paper gravitational search algorithm (GSA) is employed for optimising surface roughness and kerf width.

关键词： WEDM Taguchi's L-9 orthogonal array Surface roughness Kerf width gravitational search algorithm D2 die steel and Zinc coated wire

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