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A multi-objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm

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IET COMMUNICATIONS 2021年第18期15卷 2342-2353页

作者： Hashemi, Masoud Javaheri, Danial Sabbagh, Parisa Arandian, Behdad Abnoosian, Karlo Zabol Univ Zabol Iran Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Univ Salerno DISA MIS Dept Fisciano Italy Islamic Azad Univ Dolatabad Branch Dept Elect Engn Esfahan Iran Islamic Azad Univ Dept Stat Sci & Res Branch Tehran Iran

Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP-hard, meta-heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy-aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works.

关键词： cloud computing improved grey wolf optimization algorithm virtual machines allocation

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Kernel principal component analysis-based least squares support vector machine optimized by improved grey wolf optimization algorithm and application in dynamic liquid level forecasting of beam pump

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第6期42卷 1135-1150页

作者： Tian Zhongda Shenyang Univ Technol Coll Informat Sci & Engn Shenyang 110870 Liaoning Peoples R China

Considering the blind parameters selection and the high dimension of input data in least squares support vector machine (LSSVM) modeling process, a kernel principal component analysis (KPCA)-based LSSVM forecasting method optimized by improved grey wolf optimization (GWO) algorithm is proposed. As an excellent forecasting model, the regression forecasting performance of LSSVM is greatly affected by parameters selection of the model. An improved GWO algorithm with better performance is proposed to determine the optimal parameters of LSSVM. This improved GWO algorithm improves the optimization precision and global optimization ability of the standard GWO algorithm. The parameters of LSSVM model are taken as the optimization object that is optimized by improved GWO algorithm. At the same time, the input variables of LSSVM are correlated and redundant. KPCA algorithm can eliminate the correlation and redundancy between input variables. The reduction of input variables reduces the complexity and training time of modeling process, and the coupling between input variables, to improve the prediction accuracy of LSSVM. The dynamic liquid level of beam pump is chosen as the research object. The proposed forecasting method is applied to the prediction of dynamic liquid level. The simulation comparison on actual collected dynamic liquid level data is performed. The simulation results show that the proposed forecasting method has better predictive performance for the dynamic liquid level.

关键词： Least squares support vector machine improved grey wolf optimization algorithm kernel principal component analysis forecasting dynamic liquid level of beam pump

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Network Security Situation Prediction System Based on improved grey wolf optimization algorithm in Digital Power Enterprise Architecture 6

Network Security Situation Prediction System Based on Improv...

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6th International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Liu, Hengjie Chen, Qingshu Tang, Jie Zhuo, Junjie Lang, Jiying Cao, Wan State Grid Shandong Electric Power Company Laiyang Power Supply Company Shandong Jinan250102 China Beijing SGITG Accenture Information Technology Center Co. Ltd. Beijing100190 China Shandong Lulun Digital Technology Co. Ltd. Shandong Jinan250102 China Co. Ltd. Beijing102211 China

ISBN: (纸本)9781837242672

Artificial intelligence enables the digital transformation of power enterprise architecture. This paper studies the problem that the difficulty of network security defense in the power enterprise architecture cannot meet the existing needs. Based on the gray wolf optimization algorithm, it optimizes from three perspectives: population initialization, convergence factor and niche technology, and obtains an improved gray wolf optimization algorithm. In addition, a situation prediction model is constructed by combining the long short-term memory network, and finally a network security situation prediction system is designed. The research results show that the mean absolute error, root mean square error, mean square error and mean absolute percentage error of the prediction model are 0.0237, 0.0012, 0.0366 and 0.071 respectively, and all functions of the system have passed the test. The above results show that the research method can still complete the tasks of network security defense and early warning in the complex digital power enterprise network environment, and improve the level of network security. © The Institution of Engineering & Technology 2024.

关键词： improved grey wolf optimization algorithm Network Security Power Enterprise Situation Prediction

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optimization of Multi-layer Perceptron for Wind Power Generation Prediction Based on improved grey wolf algorithm 4th

Optimization of Multi-layer Perceptron for Wind Power Genera...

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4th International Symposium on New Energy and Electrical Technology (ISNEET)

作者： Li, Jiajun Cheng, Haitao Zhou, Xiaodong Wang, Miao Wang, Zezhao State Grid Elect Power Space Technol Co LTD Beijing 102200 Peoples R China

ISBN: (纸本)9789819770496;9789819770472;9789819770465

Wind power is a renewable energy, and its power prediction plays an important role in operation. The traditional wind power prediction model has a lot of room for improvement. This paper proposes a wind power prediction method based on improved grey wolf algorithm to optimize multi-layer perceptron, in which multi-layer perceptron is a powerful nonlinear prediction method, aiming to improve the model effect from the perspective of selecting optimization algorithms. In this study, data preprocessing is carried out first, which includes outlier filtering, missing value filling and principal component analysis. After that, the multi-layer perceptron model is constructed and trained. Secondly, considering the limitation of searching ability of standard Gray wolf algorithm, an improved Gray wolf algorithm is proposed, the convergence factor is designed as the form of adaptive decline, and the position update formula is improved for parameter optimization of multi-layer perceptron model. Finally, the data set of a wind farm in western China is used to verify the experiment. The results show that the prediction effect of the multi-layer perceptron model optimized based on the improved Gray wolf algorithm is better than other models on the verification set and the test set, and the average absolute error and the root-mean-square error are significantly reduced, which verifies the validity of the proposed method. The method proposed in this study improves the accuracy of wind power prediction and provides a new technical approach for the development and application of renewable energy.

关键词： Wind power generation Multi layer perceptron improved grey wolf optimization algorithm Correlation analysis Principal component analysis

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Research on Dynamic Economic Dispatch optimization Problem Based on improved grey wolf algorithm

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ENERGIES 2024年第6期17卷 1491页

作者： Yang, Wenqiang Zhang, Yihang Zhu, Xinxin Li, Kunyan Yang, Zhile Henan Inst Sci & Technol Sch Mech & Elect Engn Xinxiang 453003 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

The dynamic economic dispatch (DED) problem is a typical complex constrained optimization problem with non-smooth, nonlinear, and nonconvex characteristics, especially considering practical situations such as valve point effects and transmission losses, and its objective is to minimize the total fuel costs and total carbon emissions of generating units during the dispatch cycle while satisfying a series of equality and inequality constraints. For the challenging DED problem, a model of a dynamic economic dispatch problem considering fuel costs is first established, and then an improved grey wolf optimization algorithm (IGWO) is proposed, in which the exploitation and exploration capability of the original grey wolf optimization algorithm (GWO) is enhanced by initializing the population with a chaotic algorithm and introducing a nonlinear convergence factor to improve weights. Furthermore, a simple and effective constraint-handling method is proposed for the infeasible solutions. The performance of the IGWO is tested with eight benchmark functions selected and compared with other commonly used algorithms. Finally, the IGWO is utilized for three different scales of DED cases, and compared with existing methods in the literature. The results show that the proposed IGWO has a faster convergence rate and better global optimization capabilities, and effectively reduces the fuel costs of the units, thus proving the effectiveness of IGWO.

关键词： dynamic economic dispatch improved grey wolf optimization algorithm constraint-handling methods chaotic initialization nonlinear convergence factor

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A novel pixel-level aging status evaluation method on on-site post insulators

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ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Fan, Yihan Guo, Yujun Liu, Yang Ou, Yuan Yang, Chenguang Xiao, Song Zhang, Xueqin Wu, Guangning Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

In long-term operation, composite post insulators in ultra-high voltage (UHV) converter stations are prone to aging due to harsh environmental conditions such as high temperatures and intense UV radiation, leading to varying degrees of deterioration in hydrophobicity and mechanical strength of the insulators. This deterioration can subsequently trigger flashovers and power outages at the converter station, posing significant threats. Therefore, a precise and efficient detection method is proposed to effectively assess post insulators' aging status. Firstly, hyperspectral imaging (HSI) is employed to extract the spectral lines of post insulators in converter stations, and a simple and operable spray method is adopted as the standard for classifying the aging levels of the insulators. Secondly, spectral statistical characteristics (SSC) are proposed to reduce the dimensionality of hyperspectral lines, thereby improving computational efficiency. Subsequently, support vector machine (SVM), suitable for handling nonlinear hyperspectral data, is chosen as the classification model. improved grey wolf optimization (IGWO) is proposed to obtain the optimal hyperparameters for SVM. Performance metrics such as overall accuracy (OA) and Kappa are utilized to evaluate the models, comparing them with five other commonly used classification models, including extreme learning machine (ELM), long short-term memory (LSTM), back propagation neural network (BPNN), random forest (RF), and traditional SVM. The results demonstrate that the SSC-IGWO-SVM model achieves the best classification performance, with an accuracy as high as 97.5%. Finally, this model is utilized to visualize the distribution of insulator aging status. The proposed method enables pixellevel accurate evaluation on post insulators in converter stations, providing crucial assurance for the safe and reliable operation of grids.

关键词： Composite post insulator improved grey wolf optimization algorithm Hyperspectral imaging technology Spectral statistical characteristics

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A novel structure adaptive new information priority grey Bernoulli model and its application in China's renewable energy production

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RENEWABLE ENERGY 2025年 239卷

作者： Wang, Yong Yang, Zhongsen Zhou, Ying Liu, Hao Yang, Rui Sun, Lang Sapnken, Flavian Emmanuel Narayanan, Govindasami Southwest Petr Univ Sch Sci Chengdu 610500 Sichuan Peoples R China Neijiang Normal Univ Key Lab Numer Simulat Sichuan Prov Univ Coll Math & Informat Sci Neijiang 641000 Sichuan Peoples R China Univ Douala Univ Inst Technol Lab Technol & Appl Sci POB 8698 Douala Cameroon Kyungpook Natl Univ Sch Elect & Elect Engn Daegu 41566 South Korea

At present, the global energy structure is undergoing major changes. China is in the transition period of energy structure. Accurately anticipating future energy trends is critical for China's energy structure and modernization. Considering the uncertainty and sparsity characteristics of China's energy system sequence, this study examines China's renewable energy scenario using the grey model with limited sample and uncertain system modelling features. Renewable energy is affected by a variety of uncertain factors, exhibiting a range of complicated traits including nonlinearity, periodicity and random volatility. The traditional grey model has been difficult to appropriately predict its future evolution. This paper focuses on the adaptability of the model, optimizes and improves the accumulation operator and model structure, and establishes a fractional-order structural self- adaptation grey Bernoulli model based on new information priority. Firstly, based on the new information priority accumulation operator, it is extended to the fractional order. In terms of model structure, combined with NGBM(1,1) model, SADGM(1,1) model and FPGM(1,1) model, periodic fluctuation term and nonlinear power term are included to improve the model's capacity to capture nonlinear, fluctuating and periodic features, and enhance the adaptability and flexibility of the model. The backward difference technique yields the model's parameter estimation and temporal response sequence. Based on the results of the algorithm comparison experiment, the improved grey wolf optimization algorithm is chosen to optimize the structural parameters of the model in an effort to enhance its performance. The performance comparison experiment of the model was designed, and three cases of China's hydropower generation, China's renewable energy power generation installed capacity and China's solar energy quarterly power generation were selected to compare the performance with a variety of grey prediction models

关键词： Fractional order Structural self-adaptation Renewable energy prediction improved grey wolf optimization algorithm

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Energy utilization efficiency evaluation model of refining unit Based on Contourlet neural network optimized by improved grey optimization algorithm

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ENERGY 2019年 185卷 1032-1044页

作者： Zhao, Bin Ren, Yi Gao, Diankui Xu, Lizhi Zhang, Yuanyuan Liaoning Shihua Univ Sch Mech Engn Fushun 113001 Liaoning Peoples R China

Currently the total energy demand quantity is already larger than the total supply quantity, and the structural contradiction is serious, how to improve energy utilization efficiency of petrochemical enterprises is a problem that should be solved quickly. In order to correctly evaluate the energy utilization efficiency of refinery and petrochemical enterprises, the construction method of constructing energy utilization efficiency evaluation model of refining unit in petrochemical enterprises is established based on proposed Contourlet neural network optimized by improved grey algorithm. The energy utilization efficiency evaluation index system is confirmed considering its impacts. The Contourlet neural network is constructed through using Contourlet as excitation function to improve the evaluation precision. The nonlinear change strategy of controlling parameter aof traditional grey wolf optimization algorithm is proposed, the Cubic chaotic value is used as the perturbation operator of location of grey wolf to generate the new solution, and the individual memory function of Cuckoo algorithm is introduced to improve the location updating algorithm, and then the improved grey wolf optimization algorithm has better global optimization capability that is used to optimize the parameters of Contourlet neural network. The evaluation analysis of energy utilization efficiency for 250 devices for removing sulphur alcohol of liquefied gas is carried out. Results show that the proposed evaluation model has best evaluation efficiency and precision. In addition, the proposed evaluation model has highest evaluation precision of energy utilization efficiency of refinery and petrochemical enterprises. The evaluation results can effectively evaluate the energy utilization level of petrochemical enterprises, which can offer favorable theoretical basis of establishing the energy saving measurements for petrochemical enterprises. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Energy utilization efficiency Contourlet neural network improved grey wolf optimization algorithm Evaluation model

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An orthodontic path planning method based on improved gray wolf optimization algorithm

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SOFT COMPUTING 2023年第22期27卷 16589-16609页

作者： Du, Xiaozhi Yu, Ting Chen, Kai Xi An Jiao Tong Univ Sch Software Engn Shaanxi Peoples R China

Automatic tooth arrangement and path planning play an essential role in computer-aided orthodontic treatment. However, state-of-the-art methods have some shortcomings: low efficiency, excessive cost of displacements or collisions and insufficient accuracy. To address these issues, this paper proposes an innovative orthodontic path planning method based on the improved gray wolf optimization algorithm, which is called OPP-IGWO. First, the tooth model is preprocessed to obtain initial pose in which each tooth is segmented and built up with an oriented bounding box. Next, the target pose of each tooth is determined through the ideal dental arch curve and the optimal jaw principle. Finally, the path from the initial pose to the target pose of each tooth is planned based on IGWO, which is improved mainly from three aspects: (1) The greedy idea is adopted to initialize the gray wolf population based on dental interpolation. (2) The linear convergence factor in the traditional gray wolf optimization algorithm (GWO) is replaced with a nonlinear convergence factor. (3) We propose a position update strategy based on a dynamic weighting approach, which introduces a learning rate for each gray wolf. The experimental results show that our OPP-IGWO method outperforms the state-of-the-art methods. Compared with improved genetic algorithm, multiparticle improved swarm optimization, normal simplified mean particle swarm optimization algorithm, improved artificial bee colony algorithm, chaotic gray wolf optimization algorithm, hybrid gray wolf optimization algorithm and differential evolution algorithm, random walk gray wolf optimization algorithm and reinforcement learning gray wolf optimization algorithm, the OPP-IGWO has an improvement on performance by 15.46%, 2.24%, 7.18%, 15.99%, 7.67%, 1.01%, 1.42%, 10.23%, respectively.

关键词： Invisible orthodontics Orthodontic automation Orthodontic path planning improved grey wolf optimization algorithm

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Improving voltage profile of unbalanced Low-Voltage distribution networks via optimal placement and operation of distributed energy storage systems

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IET RENEWABLE POWER GENERATION 2022年第13期16卷 2748-2761页

作者： Li, Yingliang Cai, Heming Xian Shiyou Univ Sch Elect Engn Xian 710065 Peoples R China

Distributed energy storage system (DESS) has flexible operating characteristics, and DESSs can be properly configured to effectively serve the voltage regulation of the active distribution network. The existing voltage regulation-oriented DESSs optimization configuration studies are usually based on the balanced network model to analyze the impact of energy storage operation characteristics on the system voltage, but the distribution network is actually three-phase unbalanced. To this end, a method for the sequence optimization of DESSs in unbalanced distribution networks based on voltage sensitivity analysis is proposed, and the optimal configuration of DESSs in unbalanced distribution networks is studied from the perspective of improving the network voltage performance. Firstly, the DESSs placement is determined based on the proposed comprehensive voltage sensitivity analysis method;secondly, taking into account the economy of system operation, optimize the capacity of DESSs with the goal of minimizing the investment and operation and maintenance cost of DESSs, and the model solved by the improved grey wolf optimization algorithm. Finally, numerical simulations are analyzed by two unbalanced distribution testing networks. The results show that the proposed method can effectively improve the voltage profile of the unbalanced distribution network while ensuring the economy of system operation.

关键词： investment optimisation numerical simulations balanced network model sensitivity analysis energy storage power distribution economics distributed energy storage system improved grey wolf optimization algorithm system operation economy power distribution control operation characteristics voltage control voltage sensitivity analysis method unbalanced low-voltage distribution networks numerical analysis voltage regulation-oriented DESS unbalanced distribution testing networks

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