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5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Yang, Chengju Wang, Wu Lin, Tao Zhou, Shen Zhang, Ling Huang, Junxiang MinBei Vocat & Tech Coll Dept Food Nanping Peoples R China FuZhou Univ Coll Elect Engn & Automot Fuzhou Peoples R China

ISBN: (纸本)9798350363272;9798350363265

In the field of precision manufacturing, error compensation of parts is the key to improve product quality and manufacturing efficiency. This paper presents a Long Short-Term Memory Network (LSTM) model based on the gray wolf optimization algorithm designed to optimize part error compensation. First, we introduce the sources of part errors and their impact on the manufacturing process. Then, we elaborate the application of LSTM network in predicting and compensating part errors by selecting appropriate features through correlation analysis. Through experiments, we verify the effectiveness of the gray wolf optimization-based LSTM model in part error prediction and compensation. The experimental results show that compared with the traditional method, the model in this paper has a significant improvement in both error prediction accuracy and compensation efficiency.

关键词： Part error compensation gray wolf optimization algorithm Long and short-term memory networks Error prediction

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Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm

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ENERGY 2022年第0期240卷 122800-122800页

作者： Wang, Jian Xu, Yi-Peng She, Chen Xu, Ping Bagal, Hamid Asadi Chinese Acad Sci Inst Sci Beijing 100190 Peoples R China Chinese Acad Sci Inst Dev Beijing 100190 Peoples R China Shanwei Inst Technol Sch Engn Shanwei 516600 Guangdong Peoples R China Tiangong Univ Sch Math Sci Tianjin 300387 Peoples R China TianGong Univ Sch Econ & Management Tianjin 300387 Peoples R China Guangzhou Sport Univ Sch Leisure Sports & Management Guangzhou 510500 Peoples R China Shanwei Inst Technol Sch Teacher Educ Shanwei 516600 Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

Industrial and commercial use of fuel cells as a source of clean energy production are two important goals of researchers in the field of energy today. Therefore, researchers in this science are always looking for new methods for industrial production and at a reasonable price for fuel cells. This study presents a new well-organized methodology for model identification of a Solid Oxide Fuel Cell (SOFC) stack by providing optimal selection of the unknown variables in the model. The main objective here is to minimize the sum of squared error value between the designed model output voltage and the experimental data. Here, to provide an optimization process, a modified version of gray wolf optimization (MGWO) algorithm has been utilized. This algorithm is then utilized to improve the algorithm efficiency and to get better results. To show the system reliability, two scenarios based on temperature and pressure variations have been utilized. The technique has been finally compared with several other techniques to verify its prominence. With considering the achieved results, it can be observed that the sum of square error for different temperature values based on the proposed method is too small, such that for the temperatures with values 553.4 degrees C, 652.3 degrees C, 669.8 degrees C, 754.6 degrees C, and 800 degrees C the SSE value is 5.27 e-4, 2.66 e-4, 3.91 e-6, 4.19 e-3, 2.07 e-4, respectively. Furthermore, the pressure value variations from 1 atm to 5 atm with 1.44 e-3, 3.20 e-3, 5.84 e-3, 3.36 e-3, 2.67 e-3, respectively indicate its higher efficiency toward the other studied methods. Final results designate that the proposed technique delivers outstanding efficiency toward the compared methods. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： System identification Parameter estimation optimization Solid oxide fuel cell gray wolf optimization algorithm

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Improving the Mechanical Property of Dissimilar Al/Mg Zn-Added Ultrasound-Assisted Friction Stir Lap Welding Joint by Back Propagation Neural Network-gray wolf optimization algorithm

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ADVANCED ENGINEERING MATERIALS 2019年第12期21卷 1900973-1900973页

作者： Song, Qi Ren, Zhaoxu Ji, Shude Niu, Shiyu Qi, Weiwei Chen, Mingfei Shenyang Aerosp Univ Coll Aerosp Engn Shenyang 110136 Liaoning Peoples R China Liaoning Zhuanglong UAV Technol Co Ltd Zhuanglong Ind Grade Multirotor Uav Profess Techn Shenyang 110136 Liaoning Peoples R China

Process parameters of rotating velocity, welding speed, Zn interlayer thickness, and ultrasound power are optimized by the hybrid of back propagation neural network (BPNN) and gray wolf optimization algorithm (GWOA) to obtain a high-quality Zn-added ultrasound-assisted friction stir lap welding joint of 7075-T6 Al/AZ31B Mg dissimilar alloys. The results state that the prediction accuracy of the trained BPNN model is acceptable. The optimal process parameters combination is obtained by the GWOA which is combined with the trained BPNN. The verification tests are performed under the executable optimal solution, which consists of the rotating velocity of 1054 rpm, the welding speed of 54 mm min(-1), the Zn interlayer thickness of 0.05 mm, and the ultrasound power of 1568 W. The tensile shear load of the joint reaches 9.05 kN, and the strength is 11.8% larger than that of the reported optimal joint. The artificial intelligence optimization method of GWOA combined with BPNN can accurately predict and optimize the joint strength, which has great time and economic advantages.

关键词： Al Mg dissimilar alloys back propagation neural network friction stir lap welding gray wolf optimization algorithm process parameters optimization

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Maximum Power Point Tracking of Photovoltaic Module Arrays Based on a Modified gray wolf optimization algorithm

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ENERGIES 2023年第11期16卷 4329-4329页

作者： Huang, Kuo-Hua Chao, Kuei-Hsiang Kuo, Ying-Piao Chen, Hong-Han Natl Chin Yi Univ Technol Dept Elect Engn Taichung 41170 Taiwan

In this study, a modified gray wolf optimization algorithm (GWOA) was proposed to facilitate the maximum power point tracking (MPPT) of photovoltaic module arrays (PMAs). To increase the voltage conversion ratio and achieve a voltage boost through reduced duty cycles, a high-voltage step-up converter with a coupled inductor was used to replace the conventional energy storage inductor. To achieve global MPPT, the iteration parameters of the proposed GWOA were adjusted according to the slope of the PMA power-voltage (P-V) curve. According to the simulation results, the modified GWOA is more effective in MPPT than the perturbation and observation algorithm and conventional GWOA when multiple peaks appear in the P-V curve of a shaded PMA. In addition, the modified GWOA exhibits an improved tracking speed response and steady-state response.

关键词： gray wolf optimization algorithm photovoltaic module array global maximum power point tracking partial shading coupled inductor high-voltage step-up converter

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Adaptive gray wolf optimization algorithm based on Gompertz Inertia Weight Strategy

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第11期14卷 210-221页

作者： Pan, Qiuhua ShanghaiTech Univ Inst Math Sci Shanghai 201210 Peoples R China

To solve the problems that the gray wolf Optimizer (GWO) convergence speed is not fast enough and the solution accuracy is not high enough, this paper proposes an Adaptive gray wolf Optimizer based on Gompertz inertia weighting strategy (GGWO). GGWO uses the characteristics of the Gompertz function to achieve nonlinear adjustment of the inertia weight, which better balances the speed of global search and accuracy of local search of the GWO algorithm. At the same time, the Gompertz function is used to realize the adaptive adjustment of the individual gray wolf's position and to better update the gray wolves' position according to the fitness values of different gray wolf individuals. Use 6 classic test functions to compare the performance of GGWO in optimization and 10 other classic or improved swarm intelligence algorithms. Results show that GGWO has better solution accuracy, stability, and faster convergence than all other 10 swarm intelligence algorithms.

关键词： gray wolf optimization algorithm inertia weight adaptive Gompertz function swarm intelligence algorithm

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Improved gray wolf optimization algorithm Based on tanh Activation Inertia Weights 23

Improved Gray Wolf Optimization Algorithm Based on tanh Acti...

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Proceedings of the 2023 5th International Conference on Big Data Engineering and Technology

作者： Zeqiang Zhuang School of Computer Science and Engineering Hunan University of Science and Technology China

ISBN: (纸本)9798400707537

Aiming at the drawbacks of gray wolf optimization (GWO) algorithm, such as low accuracy, slow convergence, and easy to fall into local optimum, an improved gray wolf optimization algorithm based on tanh activation inertia weight (TGWO) is proposed. TGWO makes use of the characteristics of tanh function to adjust the inertia weight and balance the global optimization and local optimization, thus improving the efficiency of GWO. To verify the performance of TGWO algorithm, 8 typical test functions were used to compare the performance of TGWO with 5 classical swarm intelligence algorithms. The results show that TGWO has better optimization performance than the other 5 classical swarm intelligence algorithms.

关键词： Convergence rate gray wolf optimization algorithm Inertia weight Solution accuracy Tanh function

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Solve fractional differential equations via a hybrid method between variational iteration method and gray wolf optimization algorithm

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ASIAN-EUROPEAN JOURNAL OF MATHEMATICS 2021年第8期14卷

作者： Entesar, Ahmed Qasim, Omar Saber Univ Mosul Coll Comp Sci & Math Dept Math Mosul Iraq

In this paper, a hybrid method between Variational Iteration Method (VIM) and gray wolf optimization algorithm (GWO) was proposed to solve the fractional differential equations (FDE), where the optimal parameter value (lambda) for the VIM was estimated by the GWO. The solutions in the proposed method, GWO-VIM, demonstrated the efficiency and reliability compared to the default method VIM, by calculating the maximum absolute errors (MAE) and mean square error (MSE).

关键词： Variational iteration method gray wolf optimization algorithm nonlinear fractional differentiation equations

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Indoor Infrared Sensor Layout optimization for Elderly Monitoring Based on Fused Genetic gray wolf optimization (FGGWO) algorithm

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SENSORS 2024年第16期24卷 5393页

作者： Chen, Shuwang Chen, Yajiang Feng, Meng Hebei Univ Sci & Technol Sch informat Sci & Engn Shijiazhuang 050018 Peoples R China Hebei Prov Hosp Tradit Chinese Med Dept Acupuncture Shijiazhuang 050011 Peoples R China

With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic gray wolf optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic algorithm (GA) and the local search capability of gray wolf optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system's adaptive and real-time response capabilities.

关键词： elderly monitoring sensor layout genetic algorithm gray wolf optimization algorithm monitoring system optimization

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Enhancing the small-signal stability of the island microgrids under the virtual impedances-gray wolf optimization model: two distinct case studies

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2025年 20卷 1024-1035页

作者： Shahravi, Hamidreza Olamaei, Javad Kheradmandi, Morteza Sadr, Ali Islamic Azad Univ Dept Elect & Engn South Tehran Branch Tehran Iran Shahid Beheshti Univ Fac Elect Engn Daneshjou Blvd Velenjak Tehran Iran Iran Univ Sci & Technol Sch Elect Engn Tehran Iran

The objective of this study is to oversee the operation of several converter-based distributed generations in order to assure efficient power distribution inside an island-microgrid (MG). The study commences by introducing a MG model that integrates virtual impedances with a phase-locked loop. It subsequently presents a unique method for analyzing small-signal stability in islanded MGs. A virtual impedance setting strategy is created using the gray wolf optimization algorithm. It was found that the voltage and frequency of the MG stay within acceptable boundaries. The MG stability index was much increased and reactive power imbalances were eliminated.

关键词： small-signal stability island MGs virtual impedances distinct case studies gray wolf optimization algorithm

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Lithium battery state of health estimation based on PSO-GWO algorithm optimization under chaotic mapping with hybrid kernel extreme learning machine model

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JOURNAL OF APPLIED ELECTROCHEMISTRY 2025年第6期55卷 1383-1398页

作者： Ding, Xvqiang Ni, Yiwei Zhu, Dandan Li, Zhiwei Jiao, Yunxiao Wang, Qi Nantong Inst Technol Sch Automot Engn Nantong 226001 Peoples R China Jiangsu Univ Sci & Technol Zhenjiang 212003 Peoples R China

In battery management systems, the health status of lithium batteries constantly affects the accurate estimation of their charging status and energy status, making the health status particularly important. However, in energy storage systems, rapidly and accurately estimating the health status of lithium batteries, as well as estimating it at an appropriate proportion, has always been a challenge. Therefore, to improve the stable operation of energy storage systems, this paper proposes a model that optimizes a hybrid kernel extreme learning machine using the circle mapping chaotic method and particle swarm optimization algorithm to improve the gray wolf algorithm. First, to address the issues of initialization instability and slow convergence speed in the gray wolf optimization algorithm, the ideas of circle mapping chaos and particle swarm optimization are proposed to replace the position update formula of the gray wolf optimization algorithm, thereby enhancing the algorithm's stability and convergence speed. Second, to address the limitation of the single learning capability of the kernel extreme learning machine model, a hybrid kernel extreme learning machine model is employed. The generalization and learning capabilities of the entire model are verified through simulation experiments. Finally, simulation predictions are conducted again under different data-splitting proportions to obtain an optimal training proportion, providing additional reference directions for practical application. Experimental results demonstrate that the proposed model maintains a fit above 0.99 for four sets of batteries, and ensures the reliability and reasonableness of the model when using a 50% training set proportion.

关键词： Lithium battery state of health gray wolf optimization algorithm Hybrid kernel extreme learning machine Particle swarm optimization algorithm Circle chaos mapping

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