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A parameter adaptive identification method for a pumped storage hydro unit regulation system model using an improved gravitational search algorithm

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2017年第8期93卷 679-694页

作者： Xu, Yanhe Zhou, Jianzhong Zhang, Chu Zhang, Yuncheng Li, Chaoshun Qian, Zhongdong Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan Hubei Peoples R China

With increasing wind farm, solar farm, and pump storage plant integrations, intense frequency fluctuation of the pumped storage hydro unit (PSHU) under the no-load running condition, which is caused by its operation along the S-shaped curve, has been noted and researched. So, parameter identification of the PSHU regulation system (PSHURS) is crucial in precise modeling of the PSHU and can provide support for the optimized control and stability analysis of the power system. In this paper, a parameter adaptive identification method together with an improved gravitational search algorithm (IGSA) is proposed and applied to solve the identification problem for a PSHURS under the no-load condition. The IGSA, which is based on the standard gravitational search algorithm, accelerates convergence speed with a combination of the P-best-G(best)-guided search strategy and the adaptive elastic-ball method and improves the local optimal with the added chemotaxis operator of the bacterial foraging algorithm. Furthermore, for the parameter adaptive identification method, the parameter performance evaluator is employed to devise the moving step of the agent of the chemotaxis operator. The illustrative experiment for parameter identification of the PSHURS is used to verify the feasibility and effectiveness of the proposed method. Comparison with other methods clearly shows that the adaptive parameter identification method along with the IGSA perform best for all identification indicators.

关键词： Pumped storage hydro unit "S" characteristic pumped storage hydro unit regulation system parameter adaptive identification gravitational search algorithm chemotaxis operator

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A modified gravitational search algorithm based on a non-dominated sorting genetic approach for hydro-thermal-wind economic emission dispatching

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ENERGY 2017年 121卷 276-291页

作者： Chen, Fang Zhou, Jianzhong Wang, Chao Li, Chunlong Lu, Peng Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Hunan Peoples R China Hohai Univ Coll Environm Sci & Engn Nanjing 210098 Peoples R China

Wind power is a type of clean and renewable energy, and reasonable utilization of wind power is beneficial to environmental protection and economic development. Therefore, a short-term hydro thermal-wind economic emission dispatching (SHTW-EED) problem is presented in this paper. The proposed problem aims to distribute the load among hydro, thermal and wind power units to simultaneously minimize economic cost and pollutant emission. To solve the SHTW-EED problem with complex constraints, a modified gravitational search algorithm based on the non-dominated sorting genetic algorithm-III (MGSA-NSGA-III) is proposed. In the proposed MGSA-NSGA-III, a non-dominated sorting approach, reference-point based selection mechanism and chaotic mutation strategy are applied to improve the evolutionary process of the original gravitational search algorithm (GSA) and maintain the distribution diversity of Pareto optimal solutions. Moreover, a parallel computing strategy is introduced to improve the computational efficiency. Finally, the proposed MGSA-NSGA-III is applied to a typical hydro-thermal-wind system to verify its feasibility and effectiveness. The simulation results indicate that the proposed algorithm can obtain low economic cost and small pollutant emission when dealing with the SHTW-EED problem. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hydro-thermal-wind system Economic emission dispatch problem Parallel computing strategy gravitational search algorithm NSGA-III approach

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Reconstruction of 3D curves and surfaces using new variants of gravitational search algorithm

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JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2018年第6期39卷 1199-1221页

作者： Singh, Amarjeet Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India

The problem of reconstruction of 3D curves and surfaces is modelled as a nonlinear optimization problem in which the objective function to be minimized is the error function between given data points and data points on the generated curve/surface. Although any optimization algorithm can be used to solve this problem, but due to the inherent complexities in the optimization model, heuristic algorithms are best suited. In this paper, a recently proposed nature motivated optimization technique, namely gravitational search algorithm and its three variants are used to solve reconstruction problem. The approach is illustrated with the help of two examples- a helix and a dumbbell. The results are demonstrated and it is concluded that the approach is very promising in the area.

关键词： gravitational search algorithm Reconstruction NURBS Laplace Crossover Power Mutation optimization

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A clustering approach using a combination of gravitational search algorithm and k-harmonic means and its application in text document clustering

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第2期25卷 1251-1262页

作者： Mirhosseini, Mina Higher Educ Complex Bam Fac Math & Comp Dept Comp Sci Bam Iran

Data clustering is one of the most popular techniques of information management, which is used in many applications of science and engineering such as machine learning, pattern reorganization, image processing, data mining, and web mining. Different algorithms have been suggested by researchers, where the evolutionary algorithms are the best in data clustering and especially in big datasets. It is illustrated that GSA-KM, which is a combination of the gravitational search algorithm (GSA) and K-means (KM), is superior over some other comparative evolutionary methods. One of the drawbacks of this approach is dependency on the initial seeds. In this paper, a combination method of GSA and K-harmonic means, called GSA-KHM, has been proposed, in which the dependency on the initialization has been improved. The proposed GSA-KHM method has been applied to data clustering. As a special application, it has also been used on the text document clustering application. The simulation results show that the proposed method works better than the GSA-KM and other comparative methods in both data clustering and text document clustering applications.

关键词： Clustering gravitational search algorithm K-means K-harmonic means text clustering

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Design of a fractional-order PID controller for a pumped storage unit using a gravitational search algorithm based on the Cauchy and Gaussian mutation

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INFORMATION SCIENCES 2017年 396卷 162-181页

作者： Li, Chaoshun Zhang, Nan Lai, Xinjie Zhou, Jianzhong Xu, Yanhe Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

A pumped storage unit (PSU) is more difficult to control compared to a conventional hydropower generation unit due to the frequent switching of working conditions and the S-shaped characteristics of pump turbine. The traditional proportional integral derivative (PID) controller typically cannot easily provide high quality control. To overcome these difficulties, a fractional-order PID (FOPID) controller is designed for a PSU in this study. Although the FOPID controller is more effective compared to the traditional PID controller, it is more complex to optimize the parameters of this controller for a pump turbine governing system (PTGS). Thus, a gravitational search algorithm combined with the Cauchy and Gaussian mutation, named as CGGSA, is proposed and used to optimize the FOPID controller parameters. The experimental results indicate that the CGGSA has shown excellent optimization ability compared with some popular meta-heuristics on benchmark functions. Results have also proved that the FOPID-CGGSA controller shows significant advantages over other PID-type controllers with different optimization strategies. Meanwhile the optimally designed controller has shown great potential to improve the control quality of PTGS under multiple water heads. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Pumped storage unit Pump turbine governing system Fractional order PID controller Cauchy mutation gravitational search algorithm

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Chaotic gravitational constants for the gravitational search algorithm

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APPLIED SOFT COMPUTING 2017年 53卷 407-419页

作者： Mirjalili, Seyedali Gandomi, Amir H. Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA

In a population-based meta-heuristic, the search process is divided into two main phases: exploration versus exploitation. In the exploration phase, a random behavior is fruitful to explore the search space as extensive as possible. In contrast, a fast exploitation toward the promising regions is the main objective of the latter phase. It is really challenging to find a proper balance between these two phases because of the stochastic nature of population-based meta-heuristic algorithms. The literature shows that chaotic maps are able to improve both phases. This work embeds ten chaotic maps into the gravitational constant (G) of the recently proposed population-based meta-heuristic algorithm called gravitational search algorithm (GSA). Also, an adaptive normalization method is proposed to transit from the exploration phase to the exploitation phase smoothly. As case studies, twelve shifted and biased benchmark functions evaluate the performance of the proposed chaos-based GSA algorithms in terms of exploration and exploitation. A statistical test called Wilcoxon rank-sum is done to judge about the significance of the results as well. The results demonstrate that sinusoidal map is the best map for improving the performance of GSA significantly. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Chaos theory Chaotic map Sinusoidal map Population-based algorithm gravitational search algorithm

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Adaptive switching gravitational search algorithm: an attempt to improve diversity of gravitational search algorithm through its iteration strategy

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2017年第7期42卷 1103-1121页

作者： Ab Aziz, Nor Azlina Ibrahim, Zuwairie Mubin, Marizan Sudin, Shahdan Univ Malaya Fac Engn Kuala Lumpur 50603 Malaysia Multimedia Univ Fac Engn & Technol Melaka 75450 Malaysia Univ Malaysia Pahang Fac Elect & Elect Engn Pekan 26600 Pahang Malaysia Univ Teknol Malaysia Fac Elect Engn Skudai 81310 Johor Malaysia

An adaptive gravitational search algorithm (GSA) that switches between synchronous and asynchronous update is presented in this work. The proposed adaptive switching synchronous-asynchronous GSA (ASw-GSA) improves GSA through manipulation of its iteration strategy. The iteration strategy is switched from synchronous to asynchronous update and vice versa. The switching is conducted so that the population is adaptively switched between convergence and divergence. Synchronous update allows convergence, while switching to asynchronous update causes disruption to the population's convergence. The ASw-GSA agents switch their iteration strategy when the best found solution is not improved after a period of time. The period is based on a switching threshold. The threshold determines how soon is the switching, and also the frequency of switching in ASw-GSA. ASw-GSA has been comprehensively evaluated based on CEC2014's benchmark functions. The effect of the switching threshold has been studied and it is found that, in comparison with multiple and early switches, one-time switching towards the end of the search is better and substantially enhances the performance of ASw-GSA. The proposed ASw-GSA is also compared to original GSA, particle swarm optimization (PSO), genetic algorithm (GA), bat-inspired algorithm (BA) and grey wolf optimizer (GWO). The statistical analysis results show that ASw-GSA performs significantly better than GA and BA and as well as PSO, the original GSA and GWO.12

关键词： Asynchronous diversity gravitational search algorithm iteration strategy synchronous

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An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem

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INTERNATIONAL JOURNAL OF PRODUCTION REsearch 2017年第24期55卷 7302-7316页

作者： Ren, Yaping Yu, Daoyuan Zhang, Chaoyong Tian, Guangdong Meng, Leilei Zhou, Xiaoqiang Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China Jilin Univ Transportat Coll Changchun Jilin Peoples R China

Disassembly is indispensable to recycle and remanufacture end-of-life products, and a disassembly line-balancing problem (DLBP) is studied frequently. Recent research on disassembly lines has focused on a complete disassembly for optimising the balancing ability of lines. However, a partial disassembly process is widely applied in the current industry practice, which aims at reusing valuable components and maximising the profit (or minimising the cost). In this paper, we consider a profit-oriented partial disassembly line-balancing problem (PPDLBP), and a mathematical model of this problem is established, which is to achieve the maximisation of profit for dismantling a product in DLBP. The PPDLBP is NP-complete since DLBP is proven to be a NP-complete problem, which is usually handled by a metaheuristics. Therefore, a novel efficient approach based on gravitational search algorithm (GSA) is proposed to solve the PPDLBP. GSA is an optimisation technique that is inspired by the Newtonian gravity and the laws of motion. Also, two different scale cases are used to test on the proposed algorithm, and some comparisons with the CPLEX method, particle swarm optimisation, differential evolution and artificial bee colony algorithms are presented to demonstrate the excellence of the proposed approach.

关键词： disassembly line balancing partial disassembly profit metaheuristics gravitational search algorithm

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A novel hybrid approach based on a chaotic cloud gravitational search algorithm to complicated image template matching

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第6期25卷 4545-4557页

作者： Cui, Weijia He, Yuzhu Beihang Univ Sch Instrumentat Sci & Optoelect Engn Beijing Peoples R China

Template matching is the process of accurately extracting the interesting regions in a source image according to reference templates. In this paper, the gravitational search algorithm (GSA) is employed as a novel search strategy for template matching. However, the basic GSA is easily trapped in a local optimum and has a poor exploitation ability. In this paper, to enhance the optimization performance of GSA, a novel cross-search strategy based on chaotic global search (CGS) and cloud local search (CLS) is incorporated into GSA. The new variant is named chaotic cloud GSA (CCGSA). CGS makes full use of the ergodicity of chaos theory to improve global search ability and to avoid premature convergence. Inspired by the randomness and stable tendency of the normal cloud model, CLS was formed to realize a refined exploitation in the neighborhood of the current best solution;therefore, it can enhance optimization efficiency. Comparative experiments on six composite benchmark functions indicate that CCGSA convergence performance is superior to that of two advanced variants of GSA. Moreover, when applied to template matching, CCGSA performs better than the other selected intelligent optimization algorithms.

关键词： Template matching gravitational search algorithm chaotic global search cloud local search optimization problem

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Application of binary quantum-inspired gravitational search algorithm in feature subset selection

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APPLIED INTELLIGENCE 2017年第2期47卷 304-318页

作者： Barani, Fatemeh Mirhosseini, Mina Nezamabadi-pour, Hossein Higher Educ Complex Bam Dept Comp Sci Bam Iran Shahid Bahonar Univ Kerman Dept Elect Engn POB 76169-133 Kerman Iran

Feature selection is an important task to improve prediction accuracy of classifiers and to decrease the problem size. Several approaches have been presented to perform feature selection using metaheuristic algorithms. In this paper, we employ the binary quantum-inspired gravitational search algorithm (BQIGSA) combined with the k-nearest neighbor classifier as a wrapper approach to select a (sub-) optimal subset of features. We evaluate the proposed approach on several well-known datasets and compare our approach with other similar state-of-the-art feature selection techniques. Comparative results verify the acceptable performance of the proposed approach in feature selection.

关键词： Classification Feature selection gravitational search algorithm K-nearest neighbor Quantum computing

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