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Optimization of transformer parameters at distribution and power levels with hybrid grey wolf-whale optimization algorithm

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2023年 43卷

作者： Toren, Murat Recep Tayyip Erdogan Univ Dept Elect & Elect Engn TR-53100 Rize Turkiye

Oil-type transformers (OTT) are used more than dry-type transformers, based on cost in the transmission and distribution of electrical energy. Therefore, this usage density increases the importance of cost in OTT. Weight is important in transformer cost. The weight of the transformers depends on the variable parameters of the weights of the core and windings, C (iron cross section conformity factor) and s (cur-rent density), respectively. In this study, unlike the previous heuristic optimization studies, an innovative and complementary optimum weight was obtained by using the Gray wolf -Whale Optimization hybrid algorithm for both distribution type and power transformer type OTT. A weight reduction of 44% and approximately 14% in power transformers was achieved. It was determined that this decrease in weights provided the same reduction in OTT costs. The comparison test of the study was performed both with the values of other algorithms and statistically.& COPY;2023 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Oil -type transformer grey wolf algorithm Whale optimization algorithm Hybrid optimization

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Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling

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INTERNATIONAL JOURNAL OF DIGITAL EARTH 2025年第1期18卷

作者： Wang, He Huang, Weiquan Magnusson, Sindri Lindgren, Tony Chen, Chen Wu, Junyu Song, Yanjie Harbin Engn Univ Coll Intelligent Sci & Engn Harbin 150001 Peoples R China Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden Harbin Inst Technol Coll Mech & Elect Engn Harbin Peoples R China Dalian Maritime Univ Natl Engn Res Ctr Maritime Nav Syst Dalian Peoples R China

With the rise of low-cost launches, miniaturized space technology, and commercialization, the cost of space missions has dropped, leading to a surge in flexible Earth observation satellites. This increased demand for complex and diverse imaging products requires addressing multi-objective optimization in practice. To this end, we propose a multi-objective agile Earth observation satellite scheduling problem (MOAEOSSP) model and introduce a reinforcement learning-based multi-objective grey wolf optimization (RLMOGWO) algorithm. It aims to maximize observation efficiency while minimizing energy consumption. During population initialization, the algorithm uses chaos mapping and opposition-based learning to enhance diversity and global search, reducing the risk of local optima. It integrates Q-learning into an improved multi-objective grey wolf optimization framework, designing state-action combinations that balance exploration and exploitation. Dynamic parameter adjustments guide position updates, boosting adaptability across different optimization stages. Moreover, the algorithm introduces a reward mechanism based on the crowding distance and inverted generational distance (IGD) to maintain Pareto front diversity and distribution, ensuring a strong multi-objective optimization performance. The experimental results show that the algorithm excels at solving the MOAEOSSP, outperforming competing algorithms across several metrics and demonstrating its effectiveness for complex optimization problems.

关键词： Earth observation satellite reinforcement learning Q-learning scheduling multi-objective optimization grey wolf algorithm

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Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Chen, Jiguang Chen, Yu Nie, Rong Liu, Li Liu, Jianqiang Qin, Yuxin Zhengzhou Univ Aeronaut Sch Elect & Commun Engn Zhengzhou 450046 Peoples R China Zhengzhou Univ Aeronaut Collaborat Innovat Ctr Aeronaut & Astronaut Elect Zhengzhou 450046 Henan Peoples R China Zhengzhou Univ Aeronaut Henan Key Lab Gen Aviat Technol Zhengzhou 450046 Peoples R China

With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the grey wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution.

关键词： grey wolf algorithm Deep reinforcement learning Unmanned aerial vehicle Track planning Swarm intelligence optimization

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Channel Pruning via Improved grey wolf Optimizer Pruner

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2024年第7期E107D卷 894-897页

作者： Wang, Xueying Huang, Yuan Long, Xin Ma, Ziji Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410073 Peoples R China

In recent years, the increasing complexity of deep network structures has hindered their application in small resource constrained hardware. Therefore, we urgently need to compress and accelerate deep network models. Channel pruning is an effective method to compress deep neural networks. However, most existing channel pruning methods are prone to falling into local optima. In this paper, we propose a channel pruning method via Improved grey wolf Optimizer Pruner which called IGWOPruner to prune redundant channels of convolutional neural networks. It identifies pruning ratio of each layer by using Improved grey wolf algorithm, and then fine-tuning the new pruned network model. In experimental section, we evaluate the proposed method in CIFAR datasets and ILSVRC2012 with several classical networks, including VGGNet, GoogLeNet and ResNet-18/34/56/152, and experimental results demonstrate the proposed method is able to prune a large number of redundant channels and parameters with rare performance loss.

关键词： performance key channel pruning convolutional neural networks grey wolf algorithm fitness

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Temperature field and curing degree prediction of large composite blades based on coupled finite element analysis and machine learning

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POLYMER COMPOSITES 2025年

作者： Wu, Yue Cao, Zhong Liang Liu, Chen Wang, Xian Feng Jiangsu Inst Technol Sch Mech Changzhou Peoples R China Nanjing Univ Aeronaut & Astronaut Sch Mat Sci & Technol Nanjing Peoples R China

Since large composite blades are variable curvature and thickness components with large dimensions, the temperature field analysis will produce a large inhomogeneous temperature field, which requires a lot of time. In this paper, a new method combining finite element analysis and machine learning is proposed. By constructing a numerical model of blade temperature field curing and using zoned heating to optimize the temperature and curing gradient in the tongue and groove region, the maximum temperature gradient is reduced by 74.18% and the maximum curing degree gradient is reduced by 21.987% compared with the conventional temperature profile curing. A long short-term memory(LSTM) neural network was used to predict the temperature field and curing gradient variations, and the grey wolf algorithm was used to optimize the parameters with high model prediction accuracy. The method is instructive for online monitoring and control of the curing process in customized hot press tanks.

关键词： composite structure cure process grey wolf algorithm neural network optimization

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Integrating Intelligent Breach Detection System into 6 g Enabled Smart Grid-Based Cyber Physical Systems

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WIRELESS PERSONAL COMMUNICATIONS 2024年 1-16页

作者： Chinnasamy, P. Samrin, Rafath Ben Sujitha, B. Augasthega, R. Rajagopal, Manikandan Nageswaran, A. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Chennai India King Khalid Univ Coll Comp Sci Dept Comp Sci Abha 61421 Saudi Arabia Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumaracoil Thuckalay India St Josephs Inst Technol Dept Informat Technol Chennai 600119 India Madanapalle Inst Technol & Sci Dept CST Madanapalle 517325 India Loyola Inst Technol Dept CSE Nazarathpet Post Chennai 600123 India

This abstract explores the importance of 6G-enabled Smart Grid-based Cyber-Physical Systems (SGCPS) and how they must incorporate intelligent breach detection systems (IBDS) to keep critical infrastructure safe and efficient in the face of rapid technological change. It explores the transformative potential stemming from the convergence of 6G technology and SGCPS, offering enhanced communication capabilities and massive device connectivity while also highlighting the increased vulnerability to cyber threats. The integration of intelligent breach detection systems is proposed as a preemptive measure to identify and mitigate security breaches, fortifying critical infrastructure against emerging cyber threats. The paper provides key concepts, challenges, opportunities, and implications associated with this integration, followed by a literature review of methodologies proposed for intrusion detection and cybersecurity enhancement within SGCPS. The review discusses AI-enabled intrusion detection systems, False Data Injection (FDI) attack detection methods, anomaly-based intrusion detection systems, and adaptive robust state estimators, culminating in the introduction of a novel intrusion detection model, the AI-IBDS, leveraging the grey wolf algorithm and Artificial Neural Networks (GWAANN) for optimized detection of network intrusions in energy systems. Through comprehensive analysis and comparison of performance metrics, the proposed GWAANN method demonstrates superior efficacy in detecting critical events and enhancing cybersecurity measures compared to traditional classification methods such as SVM and KNN, underscoring the potential of AI-driven intrusion detection technologies to improve network security and resilience in SGCPS environments, ultimately paving the way for safer and more efficient energy distribution networks in the digital age.

关键词： 6G smart grid Cyber physical systems Breach detection grey wolf algorithm

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Prediction of temperature field in the whole process of instantaneous and steady state of high-speed motorized spindle

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第7-8期131卷 4021-4034页

作者： Zhang, Lixiu Bao, Ruwei Shenyang Jianzhu Univ Sch Mech Engn Shenyang Peoples R China Shenyang Jianzhu Univ Analyt & Detect Technol Res Ctr Shenyang Peoples R China Natl Local Joint Engn Lab NC Machining Equipment & Shenyang Peoples R China

The temperature rise of the high-speed motorized spindle directly affects the machining accuracy of the machine tool, making it crucial to accurately understand the dynamic changes in temperature during spindle operation in order to improve machining accuracy. This paper proposes a prediction model for the temperature field of the high-speed motorized spindle, considering both the instantaneous and steady-state phases, to accurately predict the dynamic changes in temperature rise during spindle operation. Initially, the steady-state heat transfer coefficients and the instantaneous heat generation at random moments during spindle operation are optimized using the grey wolf Optimization algorithm. Subsequently, a prediction model is established based on the identified instantaneous heat generation, using a BP neural network, to enable full-cycle prediction of the heat generation of the motorized spindle. Finally, experimental verification demonstrates that the proposed prediction model achieves a prediction accuracy of 98%.

关键词： Motorized spindle Whole process temperature field grey wolf algorithm Instantaneous heat generation Heat transfer coefficient optimization

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Multi-Source Collaborative Control Technology of Photovoltaic Power Generation Based on Differential Evolution-Gray wolf Optimization algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2024年

作者： Xu, Xuan Fang, Chao Zheng, Zhanglei Zheng, Wencheng Yu, Yang State Grid Zhejiang Quzhou Power Supply Co Quzhou Zhejiang Peoples R China

The existing photovoltaic power generation multi-source collaborative control technology cannot improve the control effect of microgrid, resulting in high peak load balancing cost of microgrid. Therefore, the photovoltaic power generation multi-source collaborative control technology based on differential evolution (DE)-gray wolf optimization (GWO) algorithm is adopted. The main components of the microgrid system, such as photovoltaic power generation equipment, hydroelectric power generation equipment, and battery pack, are modeled. With the goal of minimum peak cost and maximum transmission power, collaborative control of water, light and energy storage is carried out. The gray wolf algorithm is used to solve the multi-objective problem of water and light storage in microgrid, and the gray wolf algorithm is optimized by DE algorithm. Experiments show that the convergence speed of the optimized DEVOLU-Gray wolf algorithm is obviously accelerated, and the problem of falling into the local optimal solution is completely avoided. After the application of this method, the photovoltaic, hydraulic, and battery of the microgrid run in order, ensure the smooth operation of the microgrid, and improve the economic benefit of the microgrid.

关键词： microgrid differential evolution grey wolf algorithm collaborative control photovoltaic power generation waterpower

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SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2024年第7期238卷 1251-1260页

作者： Wu, Zhongqiang Hu, Xiaoyu Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov 438 West Hebei St Qinhuangdao 066004 Hebei Peoples R China

This paper proposes an SOC estimation method for lithium battery, which combines the online parameter identification and an improved particle filter algorithm. Targeted at the particle degradation issue in particle filtering, grey wolf optimization is introduced to optimize particle distribution. Its strong global optimization ability ensures particle diversity, effectively suppresses particle degradation, and improves the filtering accuracy. The recursive least square method with forgetting factor is also introduced to update the model parameters in a real-time manner, which further improves the estimation accuracy of SOC alternately with the improved particle filter algorithm. Experimental results validate the proposed method, with an average estimation error less than +/- 0.15%. Compared with conventional extended Kalman filter and unscented Kalman filter algorithms, the proposed algorithm has higher estimation accuracy and stability for battery SOC estimation.

关键词： Battery SOC estimation particle filter grey wolf algorithm parameter identification

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Wireless Sensor Network (WSN) Model Targeting Energy Efficient Wireless Sensor Networks Node Coverage

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IEEE ACCESS 2024年 12卷 27596-27610页

作者： Jia, Runliang Zhang, Haiyu Shanxi Finance & Taxat Coll Informat Technol Inst Taiyuan 030024 Peoples R China Taiyuan Univ Technol Coll Finance & Econ Taiyuan 030024 Peoples R China

With the rise of the Internet of Things, the application fields of wireless sensor networks (WSN) continue to expand. From agriculture to urban infrastructure monitoring, application requirements in various fields are increasing. The research focuses on designing and improving energy-efficient coverage methods for wireless sensor network nodes, with the goal of improving energy efficiency and data transmission reliability. Through detailed research and analysis of hierarchical and flat routing protocols, the article explores how to ensure that each monitoring point is covered by at least one sensor node by designing an energy-saving sensor network node coverage model. At the same time, the study explores an energy-efficient coverage method based on the improved gray wolf algorithm, aiming to optimize the deployment of sensor nodes and enhance the effectiveness of node coverage. Research results show that the algorithm performs significantly in network coverage optimization and achieves 100% coverage of monitoring target points. Under the 30-dimensional condition, the improved gray wolf algorithm shows excellent average performance and the smallest standard deviation. When the number of nodes is 40, compared with other algorithms, the improved gray wolf algorithm improves the coverage rate by 5.08% and achieves 100% coverage performance in a more energy-saving manner. Research on the exploration of energy-saving wireless sensor network models will help to better meet the needs of future intelligent monitoring and control, improve resource utilization efficiency, reduce maintenance costs, and promote the sustainable development of wireless sensor networks.

关键词： Wireless sensor network node coverage grey wolf algorithm routing protocol monitoring area

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