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International Conference on Futuristic Trends on Computational Analysis and Knowledge Management

作者： Dwaipayan Chakraborty Sankhadip Saha Samaresh Maity Dept. of Electronics and Instru. Netaji Subhash Engg. College Dept. of Electrical Engineering Netaji Subhash Engg. College

ISBN: (纸本)9781479984343

Error minimization using conventional back-propagation algorithm for training feed forward neural network (FNN) suffers from problems like slow convergence and local minima trap. Here in this paper gradient free optimization is used for error minimization to avoid local minima. Hence we introduce a new hybrid algorithm integrating the concepts of physics inspired gravitational search algorithm and biology inspired flower pollination algorithm. gravitational search algorithm is a novel meta-heuristic optimization method based on the Newtonian law of gravity and mass interaction, whereas flower pollination algorithm is an intriguing process based on the pollination characteristics of flowering plants. gravitational search algorithm efficiently evaluates global optimum but it suffers from slow searching speed in the last iterations. Flower pollination algorithm exhibits faster searching but suffers from local minima due to the switch probability. Experimental results show that hybrid FP-GSA outperforms both FPA and GSA for training FNNs in terms of classification accuracy.

关键词： Flower Pollination algorithm gravitational search algorithm Feed forward neural network Optimization Meta-heuristic

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A hybrid approach based on an improved gravitational search algorithm and orthogonal crossover for optimal shape design of concrete gravity dams.

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APPLIED SOFT COMPUTING 2014年第0期16卷 223-233页

作者： Khatibinia, M. Khosravi, Sh. Univ Birjand Dept Civil Engn Birjand Iran Islamic Azad Univ Kerman Branch Young Researchers & Elite Club Kerman Iran

A hybrid approach based on an improved gravitational search algorithm (IGSA) and orthogonal crossover (OC) is proposed to efficiently find the optimal shape of concrete gravity dams. The proposed hybrid approach is called IGSA-OC. The hybrid of IGSA and the OC operator can improve the global exploration ability of the IGSA method, and increase its convergence rate. To find the optimal shape of concrete gravity dams, the interaction effects of dam-water-foundation rock subjected to earthquake loading are considered in this study. The computational cost of the optimal shape of concrete gravity dams subjected earthquake loads is usually high. Due to this problem, the weighted least squares support vector machine (WLS-SVM) regression as an efficient metamodel is utilized to considerably predict dynamic responses of gravity dams by spending low computational cost. To testify the robustness and efficiency of the proposed IGSA-OC, first, four well-known benchmark functions in literatures are optimized using the proposed IGSA-OC, and provides comparisons with the standard gravitational search algorithm (GSA) and the other modified GSA methods. Then, the optimal shape of concrete gravity dams is found using IGSA-OC. The solutions obtained by the IGSA-OC are compared with those of the standard GSA, IGSA and particle swarm optimization (PSO). The numerical results demonstrate that the proposed IGSA-OC significantly outperforms the standard GSA, IGSA and PSO. (C) 2013 Elsevier B.V. All rights reserved.

关键词： gravitational search algorithm Orthogonal crossover Concrete gravity dams Optimal shape Weighted least squares support vector machine

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A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年第Feb期55卷 628-644页

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

Particle swarm optimization (PSO) is inspired by social behaviors of bird flocking, gravitational search algorithm (GSA) is based on the law of gravity and interaction between masses, and both of them are pertain to meta-heuristic algorithms. A novel hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA), having attributes of PSO and GSA, is proposed in this paper to solve economic emission load dispatch (EELD) problems considering various practical constraints. These constraints consist of the generator ramp rate limits, non-convex and discontinuous nature of prohibited operating zones, non-smooth characteristic of valve-point effects, multiple fuels type of generation units, and transmission losses in realistic power systems. The proposed approach embodies interesting concepts and fully incorporates the social essence of PSO with the motion mechanism of GSA. The proposed HPSO-GSA adopts co-evolutionary technique to simultaneously update particle positions with PSO velocity and GSA acceleration. HPSO-GSA, therefore, is expected to obtain an efficient balance between exploration and exploitation. From results of canonical benchmark test functions, HPSO-GSA does significantly improve PSO and GSA with better performance. As a real application, the EELD problems on five test systems including different constraints are solved by the HPSO-GSA to assess the optimizing performance of the proposed hybrid approach. The results obtained confirm the potential and effectiveness of the proposed approach compared to PSO, GSA and other algorithms published in the recent state-of-the art literatures for the solution of the EELD problems. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Particle swarm optimization gravitational search algorithm Hybrid evolutionary approach Benchmark test functions Economic emission load dispatch Practical constraints

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A novel approach for economic dispatch of hydrothermal system via gravitational search algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2014年 247卷 535-546页

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

The daily economic dispatching of hydrothermal system (DHS), which is a large-scale dynamic nonlinear constrained optimization problem, plays an important role in economic operation of electric power systems. This paper proposes a novel enhanced gravitational search algorithm (EGSA) to solve DHS problem. In the proposed method, the improvements mainly include three aspects. Firstly, particle swarm optimization (PSO) that acts complementary is integrated into gravitational search algorithm for update of agent's velocity. Secondly, heuristic search strategies based random selected dependent discharge of hydro plants and average full-load cost priority list of thermal units are adopted to deal with equality constraints of DHS problem. Thirdly, feasibility-based selection comparison techniques are devised to effectively handle inequality constraints in EGSA, which do not require penalty factors or extra parameters and can guide the agent to the feasible region quickly. The feasibility and effectiveness of the proposed EGSA method is verified by a hydrothermal test system and the simulation results are compared with those of differential evolution, PSO, genetic algorithm, classical evolutionary programming, fast evolutionary programming, and improved fast evolutionary programming algorithm. From the results, it clearly shows that the proposed method gives better quality solutions than other methods. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Cascaded reservoirs Economic dispatching gravitational search algorithm Heuristic search strategy

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Graph Planarization Problem Optimization Based on Triple-Valued gravitational search algorithm

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2014年第1期9卷 39-48页

作者： Gao, Shangce Todo, Yuki Gong, Tao Yang, Gang Tang, Zheng Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Kanazawa Univ Fac Elect & Comp Engn Brain Like Informat Proc Lab Kanazawa Ishikawa 9201192 Japan Renmin Univ China Sch Informat Beijing 100872 Peoples R China Toyama Univ Grad Sch Innovat Life Sci Toyama 9308555 Japan

This article presents a triple-valued gravitational search algorithm (TGSA) to tackle the graph planarization problem (GPP). GPP is one of the most important tasks in graph theory, and has proved to be an NP-hard problem. To solve it, TGSA uses a triple-valued encoding scheme and models the search space into a triangular hypercube quantitatively based on the well-known single-row routing representation method. The agents in TGSA, whose interactions are driven by the gravity law, move toward the global optimal position gradually. The position updating rule for each agent is based on two indices: one is a velocity index which is a function of the current velocity of the agent, and the other is a population index based on the cumulative information in the whole population. To verify the performance of the algorithm, 21 benchmark instances are tested. Experimental results indicate that TGSA can solve the GPP by finding its maximum planar subgraph and embedding the resulting edges into a plane simultaneously. Compared with traditional algorithms, a novelty of TGSA is that it can find multiple optimal solutions for the GPP. Comparative results also demonstrate that TGSA outperforms the traditional meta-heuristics in terms of the solution qualities within reasonable computational times. Published by John Wiley & Sons, Inc.

关键词： gravitational search algorithm triple-valued encoding graph planarization

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Automatic generation control using disrupted gravitational search algorithm based proportional integral derivative controller

Automatic generation control using disrupted gravitational s...

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International Conference on Recent Advances in Engineering & Computational Sciences

作者： Preeti Veena Sharma R. Naresh Harish Pulluri Electrical Engineering Department National Institute of Technology

ISBN: (纸本)9781467382540

This paper presents the solution of automatic generation control (AGC) problem using the proportional integral derivative (PID) controller optimized with the most recent meta-heuristic gravitational search algorithm (GSA) and its variant disrupted gravitational search algorithm (DGSA). A two-area hydrothermal system is considered for the performance analysis and comparison purpose. GSA and DGSA are used to search for the optimal PID parameters considering the integral time absolute error (ITAE) as the objective function. A disturbance of 0.01 p.u MW is considered for the analysis purpose. The performance is shown by the dynamic responses of frequency and tie-line deviation, their settling time and damping ratio of the system. The superiority of the DGSA optimized PID control is shown by comparing the results with GSA based PID controller. The sensitivity analysis is also carried out to show the robustness of the controller.

关键词： Automatic generation control gravitational search algorithm Disruption Hydrothermal

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GGSA: A Grouping gravitational search algorithm for data clustering

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年第0期36卷 114-121页

作者： Dowlatshahi, Mohammad Bagher Nezamabadi-pour, Hossein Lorestan Univ Dept Comp Engn Khorramabad Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

gravitational search algorithm (GSA) is a stochastic population-based metaheuristic designed for solving continuous optimization problems. It has a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. In this paper, we adapt the structure of GSA for solving the data clustering problem, the problem of grouping data into clusters such that the data in each cluster share a high degree of similarity while being very dissimilar to data from other clusters. The proposed algorithm, which is called Grouping GSA (GGSA), differs from the standard GSA in two important aspects. First, a special encoding scheme, called grouping encoding, is used in order to make the relevant structures of clustering problems become parts of solutions. Second, given the encoding, special GSA updating equations suitable for the solutions with grouping encoding are used. The performance of the proposed algorithm is evaluated through several benchmark datasets from the well-known UCI Machine Learning Repository. Its performance is compared with the standard GSA, the Artificial Bee Colony (ABC), the Particle Swarm Optimization (PSO), the Firefly algorithm (FA), and nine other well-known classical classification techniques from the literature. The simulation results indicate that GGSA can effectively be used for multivariate data clustering. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： gravitational search algorithm Grouping encoding Data clustering Nature-inspired algorithm Swarm Intelligence

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Channel assignment in multi-radio wireless mesh networks using an improved gravitational search algorithm

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2014年第0期38卷 163-171页

作者： Doraghinejad, Mohammad Nezamabadi-pour, Hossein Mahani, Ali Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

Wireless mesh networks have emerged recently to improve networking performance and other networking services. The combination of multi-radio nodes with multi-hop mesh architectures leads researcher to overcome some constraints of single-radio networks such as incapability of effective scale to exploit the increasing available system bandwidth. Therefore, a good channel assignment (CA) in multi-radio mesh networks can reduce the number of interference co-channels and improve the network throughput. In this paper, an improved version of gravitational search algorithm (IGSA) is proposed to solve CA problems;a local operator is combined with the gravitational search algorithm to find the best solution. In this study, the main goal is to minimize the overall interference and to increase the network throughput with ensuring network connectivity. The obtained results confirm the high performance of our algorithm in comparison with other related works. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Wireless mesh networks Channel assignment Multi-radios Discrete local search gravitational search algorithm

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Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

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NEURAL COMPUTING & APPLICATIONS 2014年第6期25卷 1423-1435页

作者： Mirjalili, Seyedali Wang, Gai-Ge Coelho, Leandro dos S. Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Pontifical Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Curitiba Parana Brazil Fed Univ Parana UFPR Dept Elect Engn Elect Engn Grad Program PPGEE Polytech Ctr Curitiba Parana Brazil

The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.

关键词： Binary optimization Binary algorithms PSOGSA Particle swarm optimization gravitational search algorithm

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Optimal Reconfiguration in Radial Distribution System Using gravitational search algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2014年第7期42卷 703-715页

作者： Shuaib, Y. Mohamed Kalavathi, M. Surya Rajan, C. Christober Asir Jawaharlal Nehru Technol Univ Dept Elect & Elect Engn Hyderabad Andhra Pradesh India BS Abdur Rahman Univ Dept Elect & Elect Engn Madras Tamil Nadu India JNTUH Coll Engn Hyderabad Andhra Pradesh India Pondicherry Engn Coll Dept Elect & Elect Engn Pondicherry 605014 India

This article presents an innovative technique for solving network reconfiguration problems with an objective of minimizing network (IR)-R-2 losses for an explicit set of loads. Amid many performance standards considered for optimal network reconfiguration, voltage constraint is an important one. This problem calls for determining the best combination of feeders to be opened in the radial distribution system so it provides optimal performance in the preferred settings. In solving this problem, the gravitational search algorithm is used to reconfigure the radial distribution system;this algorithm practices an optimal pattern for sustaining the radial nature of the network at every stage of the solution, and it further allows proficient exploration of the solution space. The anticipated scheme minimizes the objective function, which has been given in the problem formulation to reduce (IR)-R-2 losses in addition to balancing loads in the feeders. The solution technique involves determination of the best switching combinations and calculation of power loss and voltage profile. The practicality of the anticipated technique is validated in two distribution networks, where attained results are compared by means of available literature. Correspondingly, it is seen from the results that network losses are reduced when voltage stability is enriched through network reconfiguration.

关键词： gravitational search algorithm radial distribution system 69-bus radial distribution system distributed generation network reconfiguration I2R losses 33-bus radial distribution system tie Switches

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