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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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Fault diagnosis of transformer based on modified grey wolf optimization algorithm and support vector machine

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2020年第3期15卷 409-417页

作者： Huang, Xinyi Huang, Xiaoli Wang, Binrong Xie, Zhenyu Xihua Univ Res Inst Signal Detect & Informat Proc Technol 9999 Hongguang Ave Chengdu 610039 Peoples R China Fribourg Univ Dept Phys Ave Europe 20 CH-1700 Fribourg Switzerland

Power transformers are important pieces of equipment for the operation of power systems. Accurate diagnosis of their fault is closely related to the stable operation of the entire power grid. In order to improve the diagnostic accuracy of transformer fault, the grey wolf optimization (GWO) algorithm is introduced, and the differential evolution mechanism is integrated into the algorithm. Therefore, this paper proposes a transformer fault diagnosis method based on the modified grey wolf optimization algorithm (MGWO) and support vector machine (SVM), so that the application method realizes optimization of the penalty factor and the kernel parameter in SVM. Through the analysis of existing data examples, the SVM model optimized by the MGWO algorithm has the advantages of good generalization and strong predictive ability, and its fault diagnostic accuracy is higher than those of the genetic algorithm, particle swarm optimization algorithm, and GWO algorithm. This method has practical application significance. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： power transformer fault diagnosis grey wolf optimization algorithm differential evolution support vector machine

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High utility itemset mining: a Boolean operators-based modified grey wolf optimization algorithm

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SOFT COMPUTING 2020年第21期24卷 16691-16704页

作者： Pazhaniraja, N. Sountharrajan, S. Kumar, B. Sathis VIT Bhopal Univ Sch Comp Sci & Engn Sehore Madhya Pradesh India VIT Univ Sch Comp Sci & Engn Chennai Tamil Nadu India

In data mining, mining high utility itemset (HUI) is one among the recent thrust area that receives several approaches for solving it in an effective manner. In the past decade, addressing optimization problems using evolutionary algorithms are an unavoidable strategy due to its convergence towards optimal solution within the stipulated time. The results of evolutionary algorithms on various optimization problems are far effective when compared to the exhaustive approaches with respect to computational time. The problem with HUI is discovering a set of items from a transactional database that possess high level of utility when compared with other distinctive sets. This problem becomes harder while addressing the count of items in the database while its higher and computational time to solve this problem using exhaustive search becomes exponential as proposition of items in transaction database increases. In this paper, an optimization model based on the biological behaviour of grey wolf is proposed;the model namely grey wolf optimization algorithm is used to solve HUI using five different Boolean operations. The proposed model is evaluated using standard performance metrics over synthetic datasets and real-world datasets. The proposed model results are then compared with recent HUIM models to show the significance.

关键词： High utility itemset Boolean operators grey wolf optimization algorithm

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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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Research on Site Selection of Changsha-Zhuzhou-Xiangtan Railway Logistics Center Based on grey wolf optimization algorithm

Journal of Uncertain Systems

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Journal of Uncertain Systems 2022年第2期15卷

作者： Chen, Xin Xiao, Chenxi Shi, Huiwen Hong, Fangyu Department of Logistics Engineering Central South University Hunan 410083 China Department of Transportation Engineering Central South University Hunan 410083 China Department of Software Engineering Central South University Hunan 410083 China

With the realization of China's strategic concept of "The Belt and Road Initiative", all countries have actively carried out the planning and construction of the Trans-Asian Railway Network, and railway freight has ushered in new development opportunities. As the core cities of Hunan, Changsha, Zhuzhou and Xiangtan also actively respond to the development trend of railway freight to logistics direction. In this paper, grey wolf optimization algorithm is used for the railway logistics center location in Changsha-Zhuzhou-Xiangtan. Some data of railway freight supply points and existing freight stations in this area are used for experimental simulation, so as to reduce the investment cost of the construction of railway logistics center and provide scientific decision-making basis. © 2022 World Scientific Publishing Company.

关键词： Changsha-Zhuzhou-Xiangtan area grey wolf optimization algorithm Railway logistics center Site selection Supply point

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Path Planning of UAV for Oilfield Inspection Based on Improved grey wolf optimization algorithm 31

Path Planning of UAV for Oilfield Inspection Based on Improv...

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31st Chinese Control And Decision Conference (CCDC)

作者： Ge, Fawei Li, Kun Xu, Wensu Wang, Yi'an Bohai Univ Coll Engn Jinzhou 121013 Peoples R China

ISBN: (纸本)9781728101057

It is difficult for the traditional manual inspection method to satisfy the current management requirements. Now, UAV inspection technology has been used by more and more enterprises. In the UAV inspection, path planning is an important work. An improved grey wolf optimization algorithm is proposed for the path planning of UAV in oilfield environment in this paper. Firstly, the model of the oilfield environment is built;secondly, the initial path is produced by the basic grey wolf optimization (GWO) algorithm;and then, the fruit fly optimization (FOA) algorithm is used to continue the local optimization of the optimal solution;finally, the optimal path is generated. Compared with some other methods, the simulation results show that the improved grey wolf optimization algorithm is effective.

关键词： Oilfield inspection UAV Path planning grey wolf optimization algorithm Fruit fly optimization algorithm

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Hybrid Load Identification Model Based on grey wolf optimization algorithm 25

Hybrid Load Identification Model Based on Grey Wolf Optimiza...

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25th IEEE International Conference on Automation and Computing (IEEE ICAC)

作者： Lv Xinwei Ren Zhiren Tang Bo Liu Hui Yang Rui Wu Haiping Was Grp Co Ltd Changsha 410000 Hunan Peoples R China Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

ISBN: (纸本)9781861376657

Non-intrusive load monitoring is important for the development of smart grids. In order to get the load status and power consumption information of each device, single classifiers such as the support vector machines, the MLP neural networks and the extreme learning machines are widely used to identify the appliances. But the single classifiers are faced with the risk of local optimum and overfitting. In order to improve the recognition accuracy of the single classic classifiers, a hybrid identification model based on grey wolf optimization algorithm is proposed in this paper. The experimental results based on the actual measured data verified that the recognition accuracy of the proposed method is significantly higher than that of the single classical classification models.

关键词： Non-intrusive load identification grey wolf optimization algorithm hybrid model

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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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Optimum Wavelet-Based Homomorphic Medical Image Fusion Using Hybrid Genetic-grey wolf optimization algorithm

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IEEE SENSORS JOURNAL 2018年第16期18卷 6804-6811页

作者： Daniel, Ebenezer Vignans Fdn Sci Technol & Res Dept Elect & Commun Engn Guntur 522213 India

Medical image fusion techniques have been widely used in various clinical applications. Generalized homomorphic filters have Fourier domain features of input image. In multi-modal medical image fusion discrete wavelet transform-based techniques provides more features and is performed over Fourier spectrum. In this paper, we proposed a homomorphic wavelet fusion which is called optimum homomorphic wavelet fusion (OHWF) using hybrid genetic-grey wolf optimization (HG-GWO) algorithm. In OHWF, which consists of logarithmic and wavelet domain information of input images. The wavelet-based homomorphic fusion consists of multilevel decomposition features of input image. In our proposal, the approximation coefficients of modality1 (anatomical structure) and optimum scaled detailed coefficients of modality2 are given to adder1. In adder 2, the optimum scaled detailed coefficients of modality 1 and approximation coefficients of modality 2 are added together. The resultants of adder 1 and adder 2 are fused together using pixel based averaging rule. First, the proposed fusion approach is validated for MR-SPECT, MR-PET, MR-CT, and MR T1-T2 image fusion using various fusion evaluation indexes. Later, the conventional grey wolf optimization is modified with genetic operator. Experimental results show that the proposed approach outperforms state-of-the-art fusion algorithms in terms of both structural and the functional information in the fused image.

关键词： Homomorphic filtering wavelet transform image fusion medical imaging grey wolf optimization algorithm genetic algorithm

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Effects of dominant wolves in grey wolf optimization algorithm

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APPLIED SOFT COMPUTING 2019年 83卷 105658-000页

作者： Ozsoydan, Fehmi Burcin Dokuz Eylul Univ Fac Engn Dept Ind Engn Izmir Turkey

Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. grey wolf Optimizer (GWO), which derives inspiration from the hierarchical order and hunting behaviours of grey wolves in nature, is one of the new generation bio-inspired metaheuristics. GWO is first introduced to solve global optimization and mechanical design problems. Next, it has been applied to a variety of problems. As reported in numerous publications, GWO is shown to be a promising algorithm, however, the effects of characteristic mechanisms of GWO on solution quality has not been sufficiently discussed in the related literature. Accordingly, the present study analyses the effects of dominant wolves, which clearly have crucial effects on search capability of GWO and introduces new extensions, which are based on the variations of dominant wolves. In the first extension, three dominant wolves in GWO are evaluated first. Thus, an implicit local search without an additional computational cost is conducted at the beginning of each iteration. Only after repositioning of wolf council of higher-ranks, the rest of the pack is allowed to reposition. Secondarily, dominant wolves are exposed to learning curves so that the hierarchy amongst the leading wolves is established throughout generations. In the final modification, the procedures of the previous extensions are adopted simultaneously. The performances of all developed algorithms are tested on both constrained and unconstrained optimization problems including combinatorial problems such as uncapacitated facility location problem and 0-1 knapsack problem, which have numerous possible real-life applications. The proposed modifications are compared to the standard GWO, some other metaheuristic algorithms taken from the literature and Partic

关键词： grey wolf optimization algorithm Unconstrained optimization Constrained optimization Binary optimization Particle swarm optimization

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