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Parameter identification of a nonlinear model of hydraulic turbine governing system with an elastic water hammer based on a modified gravitational search algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2016年 50卷 177-191页

作者： Li, Chaoshun Chang, Li Huang, Zhengjun Liu, Yi Zhang, Nan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

The hydraulic turbine governing system (HTGS) is a crucial control system of hydroelectric generating units (HGUs). Parameter identification of HTGS is an important issue for the modeling and control of HGUs. The parameter identification problem of HTGS is more difficult if the elastic water hammer model is considered in the system, and existing algorithms are not effective to solve it. To solve this new problem, a modified gravitational search algorithm (MGSA) has been proposed in which modifications have been made to improve the performance of the GSA from two aspects. First, the constant attenuation factor is replaced by a hyperbolic function to generate a better gravitational constant to balance the global exploration and local exploitation during different searching stages. Second, agent mutation is introduced to increase the diversity of agents and to strengthen the ability to jump out of the local minima of the GSA. The performance of the MGSA has been verified by 13 typical benchmark problems, and the experimental results and statistical analysis demonstrate that the proposed MGSA significantly outperforms the standard GSA and some other popular optimization algorithms. The MGSA is then employed in the parameter identification of a nonlinear model of HTGS with an elastic water hammer, and the experimental results indicate that MGSA locates more precise parameter values than the compared methods. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Parameter identification Hydraulic turbine governing system Elastic water hammer gravitational search algorithm modified gravitational search algorithm

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Optimal frame selection-based watermarking using a meta-heuristic algorithm for securing video content

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 121卷

作者： Singh, Roop Pal, Raju Joshi, Deepak Noida Inst Engn & Technol Greater Noida 201306 Uttar Pradesh India Indian Inst Technol Ctr Biomed Engn Delhi 110016 India Gautam Buddha Univ Sch Informat & Commun Technol Dept Comp Sci & Engn Greater Noida 201312 Uttar Pradesh India All India Inst Med Sci AIIMS Dept Biomed Engn New Delhi 110029 India

Optimal embedding factor selection is still an open challenging issue in video watermarking. To address the same, this paper introduces a modified gravitational search algorithm (MGSA) based video watermarking (VW) scheme, termed VW-MGSA. In this proposed method, a novel variant of gravitational search algorithm i.e MGSA is employed to attain multiple optimal embedding factors (MOEF). VW-MGSA embeds watermark logo into maximum entropy blocks of size 8 x 8 followed by 1-level RDWT and Schur transform. The proposed GSA variant (MGSA) was evaluated experimentally and statistically using 22 standard benchmark functions, covering unimodal, multimodal, and fixed-dimension categories. The performance has been assessed using key metrics such as mean, standard deviation, Friedman test, and convergence graphs. These results confirm that the proposed variant outperforms existing meta-heuristic algorithms. Moreover, VW-MGSA has been validated on 8 standard benchmark videos over 19 attacks and evaluated using PSNR, SSIM, and NC metrics. The experimental and statistical results confirm that VW-MGSA outperforms existing video watermarking methods. It significantly improves the balance between imperceptibility and robustness compared to existing methods, with a measured improvement of 39.63%. The improved performance of the VW-MGSA can be applied to real-world platforms like Netflix and Amazon Prime to safeguard licensed content, with watermarks aiding in tracing piracy sources.

关键词： Video piracy Watermarking modified gravitational search algorithm (MGSA) Schur transform Redundant discrete wavelet transform (RDWT)

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Optimal operation of multicarrier energy systems using Time Varying Acceleration Coefficient gravitational search algorithm

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ENERGY 2016年第0期114卷 253-265页

作者： Beigvand, Soheil Derafshi Abdi, Hamdi La Scala, Massimo Razi Univ Dept Elect Engn Kermanshah *** Iran Politecn Bari Polytech Sch Bari Elect & Elect Dept DEE Bari Italy

This paper describes a novel modified optimization algorithm based on a new heuristic method, namely Time Varying Acceleration Coefficient gravitational search algorithm (TVAC-GSA), to solve both single and multi-objective Optimal Power Flow (OPF) problems in hybrid systems especially focusing on electricity-gas network. The suggested method is based on the Newtonian laws of gravitation and motion. Sum of the complexity of both electrical and gas-based networks in terms of the valve-point loading effect of generator units, energy hub structure, energy flow equations, and different related equality and inequality constraints make the optimization problem highly nonlinear, non-convex, non-smooth, non-differential, and high-dimensional. The effectiveness of the proposed algorithm to solve such a complex problem is verified on a new introduced hybrid system based on a modified version of IEEE 14-bus network. Comparison of results obtained by the presented method with those obtained by GSA, Particle Swarm Optimization (PSO), Genetic algorithm (GA), and Differential Evolution (DE) shows the better accuracy and fast convergence of the new method in finding an operating point with lower objective function value. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Energy hub Gas pipeline network modified gravitational search algorithm Hybrid system Multicarrier systems Multiple energy carriers Optimal operation Optimal power flow Valve point loading effect

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Application of effective gravitational search algorithm with constraint priority and expert experience in optimal allocation problems of distribution network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期117卷

作者： Qian, Jie Wang, Ping Pu, Chenggen Peng, Xiaoli Chen, Gonggui Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China

Optimal allocation problem of distribution network (OAPDN), which attracts much attention of electric enterprise, contributes to the flexible and environmental-friendly power supply by rationally introducing distributed generations (DGs) and shunt capacitors (SCs). To smoothly solve OAPDN problems, several effective measures such as advantageous schemes guidance (ASG) mechanism are presented and integrated into the proposed modified gravitational search algorithm with expert experience (MGSA-EE). Multiple OAPDN experiments essentially indicate that the MGSA-EE with higher efficiency and stronger exploration capability reduces the power loss on 33, 69 and 119 node networks by 94.15%, 98.10% and 84.59%, which is superior to most published ***, multi-objective OAPDN problem which simultaneously considers two or more goals is also studied. Compared with the single-objective one, it is more in line with the diverse demands of actual electricity market, but the difficulty is greatly increased as well. On this basis, this paper extends MGSA-EE to an innovative multi-objective MGSA-EE (MMGSA-EE) algorithm by the suggested non-inferior sorting strategy with *** typically in multi-objective OAPDN experiments on large scale networks, MMGSA-EE achieves high -quality scheme that concurrently reduces power loss and voltage deviation of 69-node network by 94.07% and 98.69%. Meanwhile, several quantitative indicators also prove that the proposed MMGSA-EE has competitive advantages over the original algorithm in terms of Pareto fronts, node voltage profiles and execution *** general, MGSA-EE and MMGSA-EE algorithms provide efficient tools for exploring superior DG/SC configuration schemes, and are of great significance to fill research gaps in multi-objective optimizations of distribution network.

关键词： Optimal allocation modified gravitational search algorithm Advantageous schemes guidance Expert experience Multi-objective optimization

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An intelligent hybrid optimistic/pessimistic concurrency control algorithm for centralized database systems using modified GSA-optimized ART neural model

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NEURAL COMPUTING & APPLICATIONS 2013年第6期23卷 1815-1829页

作者： Sheikhan, Mansour Ahmadluei, Saeed Islamic Azad Univ Dept Elect Engn South Tehran Branch Tehran Iran Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran

Concurrency control is the activity of synchronizing operations issued by concurrent executing transactions on a shared database. The aim of this control is to provide an execution that has the same effect as a serial (non-interleaved) one. The optimistic concurrency control technique allows the transactions to execute without synchronization, relying on commit-time validation to ensure serializability. Effectiveness of the optimistic techniques depends on the conflict rate of transactions. Since different systems have various patterns of conflict and the patterns may also change over time, so applying the optimistic scheme to the entire system results in degradation of performance. In this paper, a novel algorithm is proposed that dynamically selects the optimistic or pessimistic approach based on the value of conflict rate. The proposed algorithm uses an adaptive resonance theory-based neural network in making decision for granting a lock or detection of the winner transaction. In addition, the parameters of this neural network are optimized by a modified gravitational search algorithm. On the other hand, in the real operational environments we know the writeset (WS) and readset (RS) only for a fraction of transactions set before execution. So, the proposed algorithm is designed based on optional knowledge about WS and RS of transactions. Experimental results show that the proposed hybrid concurrency control algorithm results in more than 35 % reduction in the number of aborts in high-transaction rates as compared to strict two-phase locking algorithm that is used in many commercial database systems. This improvement is 13 % as compared to pure-pessimistic approach and is more than 31 % as compared to pure-optimistic approach.

关键词： Database systems Concurrency control Neural network modified gravitational search algorithm Hybrid Optimistic Pessimistic

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Multi-objective Distribution Feeder Reconfiguration Along with Optimal Sizing of Capacitors and Distributed Generators Regarding Network Voltage Security

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2022年第6-7期49卷 652-668页

作者： Lotfi, Hossein Azizivahed, Ali Shojaei, Ali Asghar Seyedi, Seyedalireza Bin Othman, Mohd Fauzi Islamic Azad Univ Dept Elect Engn Neyshabur Branch Neyshabur Iran Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia Yazd Univ Dept Math Sci Yazd Iran Univ Teknol Malaysia Int Campus Ctr Artificial Intelligence & Robot Kuala Lumpur Malaysia

Distribution network reconfiguration is one of the well-known and effective strategies in the distribution networks which performs by the status management of the network switches in order to obtain a new optimal configuration for the feeders. This study formulates multi-objective distribution feeder reconfiguration along with optimal sizing of distributed generators and capacitors. The prevalent objective functions in the network reconfiguration studies comprise of power loss and voltage deviations that are considered as the main objectives for traditional distribution systems, however, less attention has been paid to the objective functions of reliability and network voltage security in the previous literatures. Therefore, the main objective of this study is to improve the reliability and network voltage security by solving the distribution network reconfiguration problem. To this end, the energy not supplied and voltage stability index are defined as the objective functions of reliability and voltage security. A modified gravitational search algorithm is suggested to solve the complex and non-convex optimization problem. Ultimately, to demonstrate the efficiency of the proposed method;it has been tested on two 33 and 70-bus test systems, and the results are compared with the results of using other evolutionary algorithms, such as particle swarm optimization and shuffled frog leaping.

关键词： distribution network reconfiguration distributed generators modified gravitational search algorithm energy not supplied voltage stability index

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Stochastic mid-term generation scheduling incorporated with wind power

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 64卷 937-946页

作者： Niknam, Taher Massrur, Hamid Reza Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Dept Elect Engn Shiraz Branch Shiraz Iran

A challenge now facing system operator is how to schedule optimally the generation units in a wind integrated power system over a one year time horizon considering the effects of wind forecasting and variability;also, regarding the effects of load uncertainty. By the same token, this paper first develops a new formulation for Stochastic Mid-term Generation Scheduling (SMGS). In the formulation, 2m + 1 point estimate method is developed to accurately estimate the output variables of Mid-term Generation Scheduling (MGS) problem. Then, the formulation is combined with adaptive modified gravitational search algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. It is noteworthy to say that the classical methods considered certain wind information in the deterministic solution of the MGS problem which is not the realistic approach. However, this study improves modeling of wind thermal system in the MGS problem by considering possible uncertainties when scheduling the generators of power system. The proposed model is capable of taking uncertainty of load and wind into account. The proposed method is applied on two test cases and the numerical results confirmed the efficiency and stability of the proposed algorithm. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Mid-term generation scheduling Point estimate methods Wind power generation modified gravitational search algorithm Self-adaptive strategy

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Fatigue life prediction for vibration isolation rubber based on parameter-optimized support vector machine model

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FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES 2019年第3期42卷 710-718页

作者： Liu, Qiaobin Shi, Wenku Chen, Zhiyong Jilin Univ State Key Lab Automobile Simulat & Control Changchun 130022 Jilin Peoples R China

Given the small sample size, nonlinearity, and large dispersion of the measured data of fatigue performance for vibration isolation rubbers, the fatigue life prediction model for vibration isolation rubber materials was established using a support vector machine (SVM). A modified gravity search algorithm (MGSA) is proposed to optimize the parameters of the SVM. Using environmental temperature, the Rockwell hardness of the rubber compound, and the engineering strain peak as the input variables, the model was trained based on the experimental fatigue data of vibration isolation rubber materials. For comparison, the standard genetic algorithm, the standard particle swarm algorithm, and the standard simulated annealing algorithm are also implemented. Moreover, a back propagation neural network regression model is applied to the life prediction, with the conclusion that the prediction accuracy and the efficiency of MGSA are better than those of extant methods. This work can provide reference for further fatigue life prediction and structural improvement of rubber parts.

关键词： back propagation neural network life prediction modified gravitational search algorithm rubber fatigue support vector machine

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