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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2023年第4期27卷 2367-2374页

作者： Xu, Yong Lu, Xiaojuan Zhu, Yuhang Wei, Jiawei Liu, Dan Bai, Jianchong Lanzhou Jiaotong Univ Sch Automat Elect Engn Lanzhou 730070 Peoples R China

For power transformers, the gas content in oil is used as the fault input feature quantity, and the accuracy of diagnosis results is not satisfactory. The problem of low accuracy of optimized extreme learning machine (ELM) of grey wolf optimization (GWO) algorithm is proposed, and a hybrid intelligent fault diagnosis method based on random forest and improved optimized extreme learning machine of grey wolf optimization algorithm is proposed. Firstly, the importance of the candidate gas ratios is score by random forest and reassembled into five groups of feature parameters in order of importance from highest to lowest and used as the input feature quantity of the model. Secondly, the extreme learning machine is optimized to randomly generate weights and thresholds using the improved grey wolf optimization algorithm to improve the prediction accuracy of the model. Finally, the simulation experiments and comparative test analysis show that the fault diagnosis model has particular effectiveness in transformer fault diagnosis.

关键词： Fault diagnosis Extreme learning machine Random forest grey wolf optimization algorithm Power transformer

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Improved grey wolf optimization algorithm Based on Local Derivative Inertia Weights 23

Improved Grey Wolf Optimization Algorithm Based on Local Der...

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Proceedings of the 7th International Conference on algorithms, Computing and Systems

作者： Guo Chen School of Mathematics and Statistics Guangdong University of Foreign Studies China

ISBN: (纸本)9798400709098

Under the background of the development of big data and Internet economy, the effective prediction of Consumer Confidence Index (CCI) can ensure the formulation of relevant policies. In this study, to solve the problem that the grey wolf optimizer (GWO) algorithm has low optimization accuracy and slow convergence speed, the Improved grey wolf optimization algorithm Based on Local Derivative Inertia Weights (LGWO) is proposed. LGWO is a nonlinear inertia weight of derivative local formula based on information entropy and an improved grey wolf optimization algorithm that updates the position of population self-adaptively, which improves the optimization efficiency of GWO. By comparing the optimization performance of LGWO with other classical swarm intelligence algorithms through 7 typical test functions, it is confirmed that the convergence speed and solution accuracy of LGWO proposed in this paper are greatly improved compared with other classical algorithms. Subsequently, this paper establishes LGWO-BP model to predict CCI, and the simulation analysis results show that LGWO-BP model has higher prediction accuracy than ordinary BP model and GWO-BP model.

关键词： consumer confidence index grey wolf optimization algorithm inertia weight location update neural network

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Multi-UAV Three-Dimensional Path Planning Based on Improved grey wolf optimization algorithm

Multi-UAV Three-Dimensional Path Planning Based on Improved ...

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第43届中国控制会议

作者： Mingyu Zhang Feng Liu Yujie Wang Yaqing Yan ZhengXian Wei Institute of Advanced Control Systems Beijing Jiaotong University Systems Engineering Research Institute China State Shipbuilding Corporation

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To address the challenges of multi-unmanned aerial vehicle(UAV) trajectory planning in three-dimensional complex environments, this study proposes a method based on the Improved grey wolf optimization algorithm for Multi-UAV 3D Trajectory Planning. The approach simulates real geographical environments, establishing three-dimensional terrain and no-fly zone models. Building upon the foundation of single UAV trajectory planning, the proposed method incorporates collaborative constraints for multi-UAV coordination, forming an evaluation function for multi-UAV collaborative trajectory planning. In order to solve the limitations of the standard grey wolf algorithm, which is prone to local optima and exhibits suboptimal convergence rates, an improved convergence factor strategy and a reward-penalty mechanism in the optimization process are introduced. Comparative evaluations against several relevant algorithms validate the superior feasibility of the proposed approach. Simulation results demonstrate that, compared to other algorithms, the proposed method achieves smaller trajectory costs, faster convergence rates, and more stable performance.

关键词： grey wolf optimization algorithm Multi-UAV 3D Trajectory Planning Convergence Factor Reward-Penalty Mechanism

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Research on Site Selection of Changsha-Zhuzhou-Xiangtan Railway Logistics Center Based on grey wolf optimization algorithm

Journal of Uncertain Systems

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Journal of Uncertain Systems 2022年第2期15卷

作者： Chen, Xin Xiao, Chenxi Shi, Huiwen Hong, Fangyu Department of Logistics Engineering Central South University Hunan 410083 China Department of Transportation Engineering Central South University Hunan 410083 China Department of Software Engineering Central South University Hunan 410083 China

With the realization of China's strategic concept of "The Belt and Road Initiative", all countries have actively carried out the planning and construction of the Trans-Asian Railway Network, and railway freight has ushered in new development opportunities. As the core cities of Hunan, Changsha, Zhuzhou and Xiangtan also actively respond to the development trend of railway freight to logistics direction. In this paper, grey wolf optimization algorithm is used for the railway logistics center location in Changsha-Zhuzhou-Xiangtan. Some data of railway freight supply points and existing freight stations in this area are used for experimental simulation, so as to reduce the investment cost of the construction of railway logistics center and provide scientific decision-making basis. © 2022 World Scientific Publishing Company.

关键词： Changsha-Zhuzhou-Xiangtan area grey wolf optimization algorithm Railway logistics center Site selection Supply point

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Leveraging a novel grey wolf algorithm for optimization of photovoltaic-battery energy storage system under partial shading conditions

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 122卷

作者： Dagal, Idriss Ibrahim, AL-Wesabi Harrison, Ambe Beykent Univ Elect Engn Ayazaga MahallesiHadim Koruyolu Cd 9 Istanbul Turkiye Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China Univ Buea Coll Technol COT Dept Elect & Elect Engn POB 63 Buea Cameroon

Photovoltaic (PV) systems, in conjunction with battery energy storage systems (BESS), have emerged as promising solutions for sustainable energy generation and consumption. However, the performance of these systems can be significantly impacted by partial shading conditions, which can lead to power losses and reduced efficiency. This research proposes a novel grey wolf optimization algorithm (GWO) to optimize the operation of PV-BESS systems under partial shading conditions. The GWO, inspired by the hunting behavior of grey wolves, is a robust optimization technique capable of handling complex and nonlinear problems. The proposed approach aims to maximize energy output, minimize power losses, and ensure optimal battery management. By effectively addressing the challenges posed by partial shading, this research contributes to the advancement of PV-BESS systems as reliable and efficient renewable energy solutions. The proposed system, consisting of a PV array, boost converter, MPPT controller, and battery, was evaluated using MATLAB/Simulink under various conditions. The results demonstrate that the NGWO algorithm achieves 99.89 % tracking efficiency under standard conditions and over 99.26 % under PSC, outperforming particle swarm optimization (PSO), genetic algorithm (GA), the conventional GWO, and Perturb & Observe (P&O) methods. Notably, NGWO exhibits faster response times (0.01 s) and reduced power ripples compared to other algorithms, enhancing both energy extraction and battery efficiency. By optimizing state of charge (SOC) control, the NGWO extends battery lifespan, offering a superior solution for PV systems in challenging environments.

关键词： grey wolf optimization algorithm Partial shading conditions Energy storage Battery

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Strengthened grey wolf optimization algorithms for numerical optimization tasks and AutoML

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Chen, Xuefen Ye, Chunming Zhang, Yang Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China

The grey wolf optimization algorithm (GWO) is an efficient optimization technology. However, it still has some problems such as immature convergence and stagnation at local optima. In this paper, a strengthened grey wolf optimization algorithm (SGWO) is proposed based on three strengthening mechanisms: the exponential decreasing convergence factor, the elite reselection strategy in per generation and the Cauchy mutation (CM) operator. Seven variants of SGWO are designed according to different deployment modes of three reinforcement mechanisms. Experiments on thirteen numerical optimization problems are carried out to compare the differences between GWO and SGWOs. The experimental results reveal that SGWOs can significantly improve the search performance of GWO in most tasks. Among them, SGWO7 is the most successful competitor. Furthermore, several optimizers have demonstrated through comparison on engineering design problems that SGWO7 outperforms the vast majority of competitors. Subsequently, MHHO, TLBO, GWO and SGWO7 are used to build automatic machine learning (AutoML) model. The experimental results of the four methods on MNIST dataset further illustrate the advantages of SGWO7 designed in this research.

关键词： grey wolf optimization algorithm Metaheuristic Cauchy mutation AutoML

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Elite-driven grey wolf optimization for global optimization and its application to feature selection

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Zhang, Li Chen, Xiaobo Jiangsu Univ Technol Coll Comp Engn Changzhou 213001 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Peoples Bank China Changzhou City Ctr Branch Changzhou 213001 Jiangsu Peoples R China

Feature selection is crucial in data preprocessing, especially in medical data analysis. Although the grey wolf optimization (GWO) algorithm has attracted attention because of its simplicity and efficiency, it is prone to falling into the local optimum when searching fora globally optimal solution when dealing with complex feature selection problems, which restricts its application potential. To solve this problem, this paper proposes the Elite-driven grey wolf Optimizer (EDGWO) algorithm. The EDGWO algorithm significantly improves the global search capability of Alpha, Beta, and Delta grey wolves by taking advantage of the social hierarchy of the grey wolf population and designing three global exploration operators. The algorithm smoothly transitions from extensive exploration to intensive exploitation by dynamically adjusting the search parameters A. In addition, the introduced stochastic probabilistic search strategy allows omega grey wolves to make a flexible choice between local exploitation and global exploration, effectively avoiding premature convergence during the search process. To evaluate the performance of the EDGWO algorithm, this study compared twenty-two standard benchmark functions of CEC2021 and CEC2022 and twelve cancer microarray datasets. The experimental results show that the EDGWO algorithm demonstrates superior exploration and exploitation capabilities compared to fifteen well-known algorithms, with fast convergence speed and effective circumvention of local optima. Various evaluations have shown that EDGWO achieved the best Friedman rankings in the 10- and 20-dimensional CEC2021 and CEC2022 benchmark functions. In particular, the EDGWO algorithm maintains high convergence speed and high accuracy in feature selection for cancer microarray datasets.

关键词： Feature selection grey wolf optimization algorithm Elite-driven Global exploration Local exploitation Cancer microarrays

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Time Difference of Arrival Passive Location algorithm Based on grey wolf optimization 3

Time Difference of Arrival Passive Location Algorithm Based ...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Guo, Limin Xie, Qiang Huang, Xiangsong Chen, Tao Harbin Engn Univ China Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781509063529

Aiming at the nonlinear equation problems of time difference of arrival (TDOA) passive location, this paper proposes the improved grey wolf optimization (IGWO) algorithm. By adding the individual learning strategy to the standard grey wolf optimization (GWO) algorithm, and taking trigonometric function nonlinear convergence factor to balance the exploration ability and development ability, the proposed algorithm can search optimal coordinates of TDOA passive location. Simulation results show that the modified algorithm has stable performance and simple controlling parameters. The algorithm can quickly approach the global optimal solution with the small scale of wolf pack, and has faster convergence speed and higher locating precision.

关键词： passive locating time difference of arrival grey wolf optimization algorithm maximum likelihood estimation

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grey wolf Accretive Satisfaction algorithm for optimization of Residence Energy Management with Time and Device-based Preferences 8

Grey Wolf Accretive Satisfaction Algorithm for Optimization ...

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8th IEEE International Conference on Power and Energy (PECon)

作者： Ayub, Sara Ayob, Shahrin Bin Md Wei, Tan Chee Aziz, Lubna Balochistan Univ Informat Technol Engn & Manageme FICT Dept Elect Engn Quetta Pakistan Univ Teknol Johar Bahru Fac Engn Sch Elect Engn Dept Elect Engn Johar Bahru Malaysia Balochistan Univ Informat Technol Engn & Manageme FICT Dept Comp Engn Quetta Pakistan

ISBN: (纸本)9781728170688

In residential energy management (REM), time of use (TOU) of appliances scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique that capable of monitoring and controlling residential loads within a smart home. The method is based on an improved binary grey wolf accretive satisfaction algorithm (GWASA), which is founded on four hypotheses that allow time-varying preferences to be quantifiable in terms of time and devicedependent features. Based on household appliances TOU, the absolute satisfaction derived from the preferences of appliance and power ratings, the GWASA can produce optimum energy consumption pattern that will give the customer maximum satisfaction at the predefined user budget. A cost per unit satisfaction index is also established to relate daily consumer expenses with the achieved satisfaction. Simulation results on two peak budgets from $1.5/day and $ 2.5/day are carried out to analyze the efficacy of GWASA. Accordingly, the result of each of the scenarios is compared with the result obtained from three other different algorithms, namely, BPSO, BGA, BGWO. The simulation results reveal that the proposed demand side residential management based on GWASA offers the least cost per unit satisfaction and maximum percentage satisfaction in each scenario.

关键词： grey wolf optimization algorithm satisfaction index user comfort energy constraints budget constraints

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Hybrid grey wolf Optimizer with Ant Lion optimization algorithm based Deep Learning for Face Expression Recognition 2

Hybrid Grey Wolf Optimizer with Ant Lion Optimization Algori...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Jyoti Vishwanath, P. Alzubaidi, Laith H. Devika Rajesh, N. Raichur Visvesvaraya Technological University H.K.E Society's S.M.V College of Engineering Department of Ece Belagavi India H.K.E. Society's S.M.V. College of Engineering Department of Ece Karnataka Raichur India The Islamic University Najaf Iraq Kumarguru College of Liberal Arts and Science Department of Visual Communication Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

Facial expressions are an important part of recognizing human emotional messages, hence it has been a focus of pattern recognition research. However, developments in convolutional neural networks and network topologies have resulted in difficulties such as decreased overall performance and the potential loss of crucial feature information in these methods. To overcome this issue, the hybrid optimization algorithm of grey wolf optimization algorithm (GWO) based Ant Lion optimization algorithm (ALO) termed as GWALO is proposed with deep learning for recognizing facial expressions. Viola Jones algorithm and Gabor Filtering are used in preprocessing to improve image quality by minimizing redundant distortion, allowing for the extraction of relevant information. The Affine-Scale-Invariant Feature Transform is used in feature extraction to extract expressions from a preprocessed image. The hybrid GWALO is utilized in the feature selection for selecting the better features. Recurrent Neural Network (RNN) of Deep Learning (DL) is utilized in classification for classifying the various facial expressions (happiness, joy, sorrow, angry, sad and so on). The obtained result shows that the proposed GWALO algorithm attains accuracy of 99.7% precision of 98.5%, recall of 97.8%, specificity of 99.6% and f1score of 96.7% that is better when contrasted to existing optimization algorithms. © 2024 IEEE.

关键词： Ant Lion optimization algorithm Facial Expression grey wolf optimization algorithm

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