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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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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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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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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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Load balancing in cloud computing environment using the grey wolf optimization algorithm based on the reliability: performance evaluation

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JOURNAL OF SUPERCOMPUTING 2022年第1期78卷 18-42页

作者： Sefati, SeyedSalar Mousavinasab, Maryamsadat Zareh Farkhady, Roya Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran Politecn Torino Dept Management & Prod Engn Corso Duca Abruzzi 24 I-10129 Turin Italy Inst Higher Educ Roshdiyeh Dept Comp Engn Tabriz Iran

The introduction of cloud computing has brought about significant developments in information technology. Users can benefit from the multitude of cloud technology services only by connecting to the internet. In cloud computing, load balancing is the fundamental issue that has challenged experts in this research area. Load balancing helps increase user satisfaction and enhance systems' productivity through efficient and fair work assignments between computing resources. Besides, maintaining a load balancing among resources would be difficult because the resources are usually distributed in a heterogeneous way. Many load-balancing methods try to solve this problem by the metaheuristics algorithm, and each of them attempted to enhance the operation and efficiency of systems. In this paper, grey wolf optimization (GWO) algorithm has been used based on the resource reliability capability to maintain proper load balancing. In this method, first, the GWO algorithm tries to find the unemployed or busy nodes and, after discovering this node, try to calculate each node's threshold and fitness function. The results of simulation in CloudSim showed that the costs and response time in the proposed method are less than the other methods, and the obtained solutions are ideal.

关键词： Cloud computing Load balancing grey wolf optimization algorithm Reliability

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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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VMD-ATT-LSTM electricity price prediction based on grey wolf optimization algorithm in electricity markets considering renewable energy

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RENEWABLE ENERGY 2024年 236卷

作者： Xu, Yuzhen Huang, Xin Zheng, Xidong Zeng, Ziyang Jin, Tao Fuzhou Univ Dept Elect Engn Fuzhou 350116 Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia

Electricity price prediction is essential for the optimal dispatch in power markets, with accurate prediction models being critical for efficient power system operations and market trading decisions. Deep learning networks, with their powerful nonlinear modeling capabilities, have shown promising results in electricity price forecasting. However, their design techniques, especially the selection of network parameters, remain challenging. This indicates that the optimization and exploration of deep learning networks in electricity price forecasting models require further investigation. This paper innovatively proposes a forecasting model that uniquely integrates Variational Mode Decomposition (VMD), grey wolf optimization (GWO), Attention Mechanism (ATT), and Long Short-Term Memory Network (LSTM), optimizing the model from three different perspectives. First, during the data preprocessing phase, the training set is subjected to VMD to reduce noise, thereby enhancing the capture of multi-scale characteristics inherent in electricity price time series. The ATT layer is integrated to adaptively allocate weights, enhancing the model's focus on significant features. The GWO is applied to optimize hyperparameters of the LSTM, accelerating convergence and improving iteration accuracy, thereby reducing model error. A series of experiments were conducted using multiple regional electricity price datasets, evaluated with several metrics including RMSE. The results validated the effectiveness of the proposed three modules in improving the performance of the time series network, with VMD making the most significant contribution. Among all models, VMD-GWO-ATT-LSTM consistently outperformed others, demonstrating its effectiveness and robustness in electricity price forecasting.

关键词： Electricity price prediction Long-short term memory Attention mechanism grey wolf optimization algorithm Variational mode decomposition

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