Threshold-Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic-Based Internal Defect Detection of Arc Magnets

The acoustic-based internal defect detection is essential to ensure the quality of arc magnets efficiently. Swarm decomposition (SWD) is conducive to processing acoustic signals, but it is still confronted with threshold optimization problems. Especially, the existing optimization methods for the SWD thresholds are merely available for a single signal with exclusive characteristics, instead of the various signals with similar characteristics. Therefore, a threshold-optimized SWD using grey wolf optimizer (GWO) is proposed to solve these issues and applied to detect the internal defects of arc magnets. In this method, a fitness function is designed to indicate the relationship between the SWD thresholds and the overall decomposition effect of similar signals. The minimum value of it corresponds to the threshold setting yielding the optimal decomposition. GWO is used for searching such a minimum value, and the obtained optimal threshold setting allows SWD to decompose any signal into a series of oscillatory components. The frequency information in the two oscillatory components with the highest energy ratio is extracted as the internal defect features. Random forest is carried out to identify these features. Experimentally, the detection accuracy reaches above 97%, and the detection speed per single arc magnet does not exceed 3.4 seconds. The proposed method cannot only determine the unified threshold setting of SWD for similar signals but also achieve an accurate, rapid detection for the internal defects.

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Penalty Strategy in The Fitness Function of Grey Wolf Optimizer for Minimum Spanning Tree Problem

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1077/1/012071 ◽

2021 ◽

Vol 1077 (1) ◽

pp. 012071

Author(s):

Z Zukhri ◽

I V Paputungan ◽

N S Handayani ◽

V Satriadi

Keyword(s):

Spanning Tree ◽

Minimum Spanning Tree ◽

Fitness Function ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Minimum Spanning Tree Problem ◽

Penalty Strategy

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Combined Convolutional and LSTM Recurrent Neural Networks for Internal Defect Detection of Arc Magnets Under Strong Noises and Variable Object Types

IEEE Access ◽

10.1109/access.2021.3078709 ◽

2021 ◽

pp. 1-1

Author(s):

Qiang Li ◽

Qinyuan Huang ◽

Ying Zhou ◽

Tian Yang ◽

Maoxia Ran ◽

...

Keyword(s):

Neural Networks ◽

Defect Detection ◽

Recurrent Neural Networks ◽

Internal Defect ◽

Internal Defect Detection

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An elephant herd grey wolf optimization (EHGWO) algorithm for load balancing in cloud

International Journal of Pervasive Computing and Communications ◽

10.1108/ijpcc-09-2019-0070 ◽

2020 ◽

Vol 16 (3) ◽

pp. 259-277

Author(s):

Pooja Arora ◽

Anurag Dixit

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

System Performance ◽

Virtual Machines ◽

Fitness Function ◽

Optimization Technique ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Content Type ◽

Computing Model

Purpose The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. However, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing (LB) method for allocating tasks to virtual machines (VMs) without influencing system performance. This paper aims to develop a technique for LB in cloud using optimization algorithms. Design/methodology/approach This paper proposes a hybrid optimization technique, named elephant herding-based grey wolf optimizer (EHGWO), in the cloud computing model for LB by determining the optimal VMs for executing the reallocated tasks. The proposed EHGWO is derived by incorporating elephant herding optimization (EHO) in grey wolf optimizer (GWO) such that the tasks are allocated to the VM by eliminating the tasks from overloaded VM by maintaining the system performance. Here, the load of physical machine (PM), capacity and load of VM is computed for deciding whether the LB has to be done or not. Moreover, two pick factors, namely, task pick factor (TPF) and VM pick factor (VPF), are considered for choosing the tasks for reallocating them from overloaded VM to underloaded VM. The proposed EHGWO decides the task to be allocated in the VM based on the newly derived fitness functions. Findings The minimum load and makespan obtained in the existing methods, constraint measure based LB (CMLB), fractional dragonfly based LB algorithm (FDLA), EHO, GWO and proposed EHGWO for the maximum number of VMs is illustrated. The proposed EHGWO attained minimum makespan with value 814,264 ns and minimum load with value 0.0221, respectively. Meanwhile, the makespan values attained by existing CMLB, FDLA, EHO, GWO, are 318,6896 ns, 230,9140 ns, 1,804,851 ns and 1,073,863 ns, respectively. The minimum load values computed by existing methods, CMLB, FDLA, EHO, GWO, are 0.0587, 0.026, 0.0248 and 0.0234. On the other hand, the proposed EHGWO with minimum load value is 0.0221. Hence, the proposed EHGWO attains maximum performance as compared to the existing technique. Originality/value This paper illustrates the proposed LB algorithm using EHGWO in a cloud computing model using two pitch factors, named TPF and VPF. For initiating LB, the tasks assigned to the overloaded VM are reallocated to under loaded VMs. Here, the proposed LB algorithm adapts capacity and loads for the reallocation. Based on TPF and VPF, the tasks are reallocated from VMs using the proposed EHGWO. The proposed EHGWO is developed by integrating EHO and GWO algorithm using a new fitness function formulated by load of VM, migration cost, load of VM, capacity of VM and makespan. The proposed EHGWO is analyzed based on load and makespan.

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EKEGWO: Enhanced Kernel-Based Exponential Grey Wolf Optimizer for Bi-Objective Data Clustering

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488519500296 ◽

2019 ◽

Vol 27 (04) ◽

pp. 669-688 ◽

Cited By ~ 1

Author(s):

Amolkumar Narayan Jadhav ◽

Gomathi N.

Keyword(s):

Data Clustering ◽

Clustering Algorithm ◽

Fitness Function ◽

Multidimensional Data ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Widespread Application ◽

Clustering Techniques ◽

Cluster Distance ◽

F Measure

The widespread application of clustering in various fields leads to the discovery of different clustering techniques in order to partition multidimensional data into separable clusters. Although there are various clustering approaches used in literature, optimized clustering techniques with multi-objective consideration are rare. This paper proposes a novel data clustering algorithm, Enhanced Kernel-based Exponential Grey Wolf Optimization (EKEGWO), handling two objectives. EKEGWO, which is the extension of KEGWO, adopts weight exponential functions to improve the searching process of clustering. Moreover, the fitness function of the algorithm includes intra-cluster distance and the inter-cluster distance as an objective to provide an optimum selection of cluster centroids. The performance of the proposed technique is evaluated by comparing with the existing approaches PSC, mPSC, GWO, and EGWO for two datasets: banknote authentication and iris. Four metrics, Mean Square Error (MSE), F-measure, rand and jaccord coefficient, estimates the clustering efficiency of the algorithm. The proposed EKEGWO algorithm can attain an MSE of 837, F-measure of 0.9657, rand coefficient of 0.8472, jaccord coefficient of 0.7812, for the banknote dataset.

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