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...
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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.
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 d...
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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 greywolfoptimization (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 optimizationalgorithm, and GWO algorithm. This method has practical application significance. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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 ...
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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 greywolf 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.
For a high search accuracy and overcoming the problem of tangling the local optimum of greywolfoptimization (GWO) algorithm, a nonlinear convergence factor combining tangent and logarithmic functions is proposed to ...
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ISBN:
(纸本)9781728118598
For a high search accuracy and overcoming the problem of tangling the local optimum of greywolfoptimization (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 greywolfalgorithm in convergence speed, stability and optimization accuracy.
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 ...
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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 plann...
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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 greywolfoptimization (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.
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, ...
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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.
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 loc...
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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.
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...
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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-greywolfoptimization (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 greywolfoptimization 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.
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 simpl...
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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. greywolf 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
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