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Research on hierarchical emergency resource scheduling for island petrochemical enterprises based on improved multi-objective grey wolf optimization algorithm

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ENERGY 2025年 322卷

作者： Ye, Jihong Shi, Ren Guo, Chuanqi Zhejiang Ocean Univ Natl & Local Joint Engn Res Ctr Harbor Oil & Gas S Sch Petrochem Engn & Environm Zhejiang Key Lab Pollut Control Port Petrochem Ind 1 Haida South Rd Zhoushan 316022 Peoples R China

Island petrochemical enterprises, as high-risk entities within the energy sector, face severe challenges in ensuring operational safety. Therefore, the ability to quickly and efficiently dispatch emergency resources after an accident occurs is crucial to prevent escalation of the situation. This paper constructs a hierarchical emergency resource scheduling model based on the hierarchical response to accidents in petrochemical enterprises, using an Improved Multi-objective grey wolf optimization (IMOGWO) algorithm to solve the problem and optimize the emergency resource scheduling scheme. Key algorithm enhancements include a novel encoding method, incorporation of crossover and mutation operators to refine the predation strategy, and the use of Sobol sequences, reverse learning, and random disturbances for improved population initialization. Finally, taking a certain island petrochemical enterprise in China as an example, the effectiveness and practicality of the model were verified. Compared with the actual scheme, the optimized emergency resource scheduling scheme reduced the total cost by 38.50 % and the dispatch time by 20.81 % at the intra-factory level (enterprise). In terms of the extra-factory level (municipal), the total cost and the dispatch time were reduced by 25.33 % and 14.55 %, respectively. This study contributes to the sustainable and safe development of petrochemical enterprises for a secure energy future.

关键词： Emergency response framework Multi-objective optimization Emergency logistics Petrochemical accidents grey wolf optimization algorithm

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State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries

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ENERGY 2025年 315卷

作者： Peng, Simin Wang, Yujian Tang, Aihua Jiang, Yuxia Kan, Jiarong Pecht, Michael Yancheng Inst Technol Sch Elect Engn Yancheng 224051 Peoples R China Chongqing Univ Technol Key Lab Adv Mfg Technol Automobile Parts Minist Educ Chongqing 400054 Peoples R China Univ Maryland Ctr Adv Life Cycle Engn College Pk MD 20742 USA

Accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for the safety and operation of electric vehicles. The accuracy and efficiency of SOH estimation are degraded by existing data-driven methods that depend on the empirically selecting hyperparameters and time-consuming aging data. In this paper, a method joint improved grey wolf optimization (IGWO) algorithm and long short-term memory (LSTM) is developed to estimate the SOH using partial discharging health features (HFs). A dropout technique is applied to overcome the overfitting issue of the LSTM for SOH estimation. An IGWO algorithm is presented to address the challenges of the GWO algorithm, such as its tendency to fall into local optimization used with an LSTM, for accurately obtaining the optimal hyperparameters of LSTM. To reduce the consuming time of aging data, the LSTM model joint the IGWO is developed to estimate the SOH using partial discharging HFs. Compared to using five HFs, the experimental results demonstrate that the SOH can be estimated accurately by the developed method using two HFs in shorter consuming time with the mean absolute error, root mean square error, mean absolute percentage error, and average values of all of them within 1 %.

关键词： State of health grey wolf optimization algorithm Long short-term memory Partial health features

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A Fast Satellite Selection Method Based on the Multi-Strategy Fusion grey wolf optimization algorithm for Low Earth Orbit Satellites

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REMOTE SENSING 2025年第8期17卷 1320-1320页

作者： Lv, Wei Chen, Mingjian Shi, Xingyu Li, Yuxing Shen, Yang Li, Wanli Tong, Shuai Informat Engn Univ Inst Geospatial Informat Zhengzhou 450001 Peoples R China Wuhan Univ Minist Educ Key Lab Polar Environm Monitoring & Publ Governanc Wuhan 430071 Peoples R China Key Lab Smart Earth Beijing 100094 Peoples R China

Low Earth Orbit (LEO) satellites utilizing Doppler measurements can be an effective supplementary positioning solution when Global Navigation Satellite System (GNSS) signals are unavailable. LEO satellites pose challenges to the efficiency and stability of real-time satellite selection algorithms due to their high dynamic and large number. The traditional satellite selection algorithms have the problems of high computational complexity and significant hardware dependence. In contrast, the intelligent optimization algorithm significantly improves the accuracy and real-time performance of satellite selection through global search and efficient processing. According to the characteristics of LEO satellites, a Multi-Strategy Fusion grey wolf optimization (MSFGWO) algorithm is proposed for satellite selection. The experimental results show that when six satellites are selected, the average Doppler Geometric Dilution of Precision (DGDOP) value of the MSFGWO algorithm is 222.08. Compared with the DGDOP ratio of the traversal method, it is 1.03. The three-dimensional positioning accuracy is 192.86 m, and the positioning error is improved by 54.43% compared with the positioning accuracy of the traditional GWO algorithm. The longest continuous observation was achieved for 45 s, during which no switching of six satellites occurred at adjacent moments. The calculation time of the algorithm was only 0.0174 s, and the efficiency was improved by 93.43%. The MSFGWO algorithm proposed in this paper not only improves the overall optimization ability of the grey wolf Optimizer (GWO) algorithm and effectively reduces the DGDOP value but also significantly reduces the satellite switching number and prolongates the continuous observation time, thus improving the stability and accuracy of the positioning solution.

关键词： LEO constellation satellite selection grey wolf optimization algorithm computational efficiency

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grey wolf optimization algorithm-based robust neural learning control of passive torque simulators with predetermined performance

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2024年第1期32卷 126-143页

作者： Saadat, Seyyed Amirhossein Fateh, Mohammad Mehdi Keighobadi, Javad Shahrood Univ Technol Semnan Iran

In flight control systems, the actuators need to tolerate aerodynamic torques and continue their operations without interruption. To this end, using the simulators to test the actuators in conditions close to the real flight is efficient. On the other hand, achieving the guaranteed performance encounters some challenges and practical limitations such as unknown dynamics, external disturbances, and state constraints in reality. Thus, this article attempts to present a robust adaptive neural network learning controller equipped with a disturbance observer for passive torque simulators (PTS) with load torque constraints. The radial basis function networks (RBFNs) are employed to identify the unknown terms, providing information for the disturbance observer. Besides, the tuning parameters are chosen optimally by adopting the grey wolf optimization (GWO) algorithm. The closed -loop system stability is also proven by the barrier Lyapunov function (BLF) while the total uncertainties, including system dynamics, friction, and disturbance, are tracked by the total estimation. Thus, the predetermined performance, robust behavior, and high -precision estimation are the achievements of the presented controller for PTS. To confirm the ability of the proposed control idea, simulations are provided. Furthermore, a comparison scenario is also considered to emphasize the supremacy of the proposed control system.

关键词： Neural network passive torque simulator state constraint disturbance observer grey wolf optimization algorithm robust control

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grey wolf optimization algorithm with improved convergence factor and position update strategy 11

Grey wolf optimization algorithm with improved convergence f...

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11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Yan, Chunman Chen, Jiahui Ma, Yunting Northwest Normal Univ Coll Phys & Elect Engn Lanzhou Peoples R China

ISBN: (纸本)9781728118598

For a high search accuracy and overcoming the problem of tangling the local optimum of grey wolf optimization (GWO) algorithm, a nonlinear convergence factor combining tangent and logarithmic functions is proposed to dynamically adjust the global search ability of the algorithm. An adaptive position updating strategy is also introduced to accelerate the convergence speed of the algorithm in the process of convergence. The experimental results on benchmark functions show that the improved algorithms outperform the standard grey wolf algorithm in convergence speed, stability and optimization accuracy.

关键词： grey wolf optimization algorithm convergence factor position updating strategy

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grey wolf optimization algorithm FOR MAXIMUM ANNUAL SAVINGS IN RADIAL DISTRIBUTION SYSTEMS 2

GREY WOLF OPTIMIZATION ALGORITHM FOR MAXIMUM ANNUAL SAVINGS ...

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2nd International Conference on Computing Methodologies and Communication (ICCMC)

作者： Prasad, C. Hari Subbaramaiah, K. Sujatha, P. JNTUA Anantapuram Andhra Pradesh India YITS Tirupati Andhra Pradesh India JNTUA Dept EEE Anantapuram Andhra Pradesh India

ISBN: (纸本)9781538634523

This paper exhibits a two phase approach that decides the ideal location and size of capacitors in distribution systems to enhance voltage profile and to decrease the real power loss. In first stage, the capacitor locations can be found by utilizing loss sensitivity method. grey wolf optimization algorithm is utilized for finding the ideal capacitor sizes. The sizes of the capacitors consequent to most extreme yearly savings are taking into account by the capacitors cost. The proposed technique has been applied on 15, 34 and 69-bus test systems and the results are compared with existing algorithm.

关键词： Capacitor Placement Loss Sensitivity Method grey wolf optimization algorithm Radial Distribution System

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Hybrid grey wolf optimization algorithm Incorporating Reinforcement Learning for Solving EEDHFSP

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Engineering Letters 2025年第5期33卷 1623-1631页

作者： Zhang, Qi Wen Liu, Xi Chen School of Computer and Communication Lanzhou University of Technology Gansu Lanzhou 730050 China

In this paper, the grey wolf optimization algorithm based on reinforcement learning (GWOBRL) algorithm is proposed. It is designed to address the energy-efficient distributed hybrid flow shop scheduling problem (EEDHFSP). This is achieved by combining Navigable Small World (NSW) neighborhood structure and reinforcement learning. The NSW structure enables the algorithm to accurately select the learning objects to avoid the decline of population diversity. Reinforcement learning integrates the population state and prior knowledge to select the best update strategy for individuals and improve search efficiency. In order to accurately evaluate population diversity, a comprehensive evaluation mechanism was constructed. This mechanism combines Euclidean distance and cosine distance to solve the problem of evaluation distortion. The experimental results show that the GWOBRL algorithm is better than other algorithms in most cases, and compared with the traditional methods, it can significantly improve the search effect while maintaining population diversity, showing higher performance and practical value, and providing an effective solution to the energy-efficient scheduling problem of the distributed hybrid flow shop. © 2025, International Association of Engineers. All rights reserved.

关键词： Distributed hybrid flow shop scheduling energy-efficient scheduling grey wolf optimization algorithm neighborhood structure reinforcement learning

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A new fuzzy-based method for load balancing in the cloud-based Internet of things using a grey wolf optimization algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2020年第8期33卷 e4370-e4370页

作者： Li Xingjun Shao Zhiwei Cheng Hongping Mohammed, Bayan Omar Xian Eurasia Univ Xian 710065 Shaanxi Peoples R China Univ Human Dev Dept Informat Technol Sulaymaniyah Iraq

Cloud computing provides high accessibility, scalability, and flexibility in the era of computing for different practical applications. Internet of things (IoT) is a new technology that connects the devices and things to provide user required services. Due to data and information upsurge on IoT, cloud computing is usually used for managing these data, which is known as cloud-based IoT. Due to the high volume of requirements, service diversity is one of the critical challenges in cloud-based IoT. Since the load balancing issue is one of the NP-hard problems in heterogeneous environments, this article provides a new method for response time reduction using a well-known grey wolf optimization algorithm. In this paper, we supposed that the response time is the same as the execution time of all the tasks that this parameter must be minimized. The way is determining the status of virtual machines based on the current load. Then the tasks will be removed from the machine with the additional load depending on the condition of the virtual machine and will be transferred to the appropriate virtual machine, which is the criterion for assigning the task to the virtual machine based on the least distance. The results of the CloudSim simulation environment showed that the response time is developed in compared to the HBB-LB and EBCA-LB algorithm. Also, the load imbalancing degree is improved in comparison to TSLBACO and HJSA.

关键词： cloud-based IoT CloudSim grey wolf optimization algorithm imbalancing degree load balancing response time

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Fractional-order filter-based enhanced full state feedback control design with multi-objective formulation using grey wolf optimization algorithm

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OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第4期44卷 1854-1872页

作者： Kumar, Lalitesh Dhillon, Sukhwinder Singh Kumar, Prawendra Zhejiang Univ Coll Control Sci & Engn Hangzhou Zhejiang Peoples R China Ajay Kumar Garg Engn Coll Dept Elect & Elect Engn Ghaziabad Uttar Pradesh India J P Morgan Chase & Co Warsaw Poland Zhejiang Univ Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

A perturbed fractional-order filter (FOF)-based LQR control design with multiple performance indices is proposed in this paper. Three variants of the FOF-based linear quadratic regulator (LQR) system have been proposed along with choices on the commensurate order of state feedback law. The multi-objective problem formulation has been done with three performance objectives to address the different issues of design. The first performance index constitutes a weighted sum of ITAE (Integral Time of Absolute Error) and the difference between the eigenvalues of the plant and the LQR controlled plant, chosen to minimize the large oscillations as well as relative stability with respect to eigenvalues. A new lower bound of the singular values have been addressed to ensure the robust stability of the system through the formulation of a second performance objective. It is the minimization of singular values of the return ratio matrix at the plant's input. The third and the last performance index aims minimization of maximum singular values of the perturbation transfer function at the plant's output, so as to guarantee no closed-loop right half plane zeros. The multi-objective optimization problem is solved and the optimal solutions are obtained via grey wolf optimization algorithm. The simulation results validate the performance and effectiveness of the proposed control design.

关键词： fractional order control grey wolf optimization algorithm LQR multi-objective optimization

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LQR Pendulation Reduction Control of Ship-Mounted Crane Based on Improved grey wolf optimization algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2023年第3期24卷 395-407页

作者： Sun, Mingxiao Ji, Changyu Luan, Tiantian Wang, Nan Harbin Univ Sci & Technol Sch Automation 52 Xuefu Rd Harbin 150080 Heilongjiang Peoples R China Harbin Univ Sci & Technol Heilongjiang Prov Key Lab Complex Intelligent Syst 52 Xuefu Rd Harbin 150080 Heilongjiang Peoples R China Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Minist Educ 52 Xuefu Rd Harbin 150080 Heilongjiang Peoples R China Wuxi CREG Urban Rail Transit Equipment Co Ltd 1061Jiaoshan Rd Wuxi 214000 Jiangsu Peoples R China

The poor adaptability matrix of traditional LQR controller causes the problems of large payload swing and slow response for ship-mounted cranes during operation. To solve such problems, an LQR controller based on an improved grey wolf optimization algorithm (IGWO-LQR) is proposed. Firstly, the dynamics model of ship-mounted crane is constructed, the pendulum reduction problem is transformed into the LQR quadratic performance index problem, and IGWO is used to optimize the weight matrix. At the same time, the RBF neural network is applied to compensate for the non-linear wave disturbances in the system. Finally, the pendulum reduction efficiency of the controller under different parameters and conditions is verified by numerical simulation. Compared with the traditional LQR controller, the simulation results show that the control accuracy of the IGWO-LQR controller is improved by about 5%, and the response speed is improved by about 5-10 s. This method can significantly reduce the payload swing and improve work efficiency.

关键词： Ship-mounted crane LQR controller grey wolf optimization algorithm Parameter optimization RBF neural network

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