Energy efficient load balancing approach for avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function

2019 ◽  
Vol 84 ◽  
pp. 105706 ◽  
Author(s):  
Amruta Lipare ◽  
Damodar Reddy Edla ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Load Balancing ◽  
Energy Efficient ◽  
Fitness Function ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Energy Hole ◽  
Energy Hole Problem

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

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.

scholarly journals The Hybrid Optimization Algorithm for Load Balancing in Cloud

2019 ◽  
Vol 8 (5S3) ◽  
pp. 67-71 ◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Fitness Function ◽  
Research Direction ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Network Access ◽  
Cloud Infrastructure ◽  
Shared Resources

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. But, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing method for allocating tasks to Virtual Machines (VMs) without influencing system performance. This paper proposes a load balancing technique, named Elephant Herd Grey Wolf Optimization (EHGWO) for balancing the loads. The proposed EHGWO is designed by integrating Elephant Herding Optimization (EHO) in Grey Wolf Optimizer (GWO) for selecting the optimal VMs for reallocation based on newly devised fitness function. The proposed load balancing technique considers different parameters of VMs and PMs for selecting the tasks to initiate the reallocation for load balancing. Here, two pick factors, named Task Pick Factor (TPF) and VM Pick Factor (VPF), are considered for allocating the tasks to balance the loads.

Multi-Objective Decision Making Approach based Load Balanced Clustering in WSNs to Increase The Lifetime of Network

2021 ◽  
Author(s):  
Lekhraj ◽  
Alok Kumar ◽  
Anoj Kumar
Keyword(s):  
Energy Efficient ◽  
Wireless Sensors ◽  
Fitness Function ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Effective Technique ◽  
Successful Approach ◽  
Balanced Clustering

Abstract A network of wireless sensors (WSN) is an outstanding technology that can aid in the various applications. Batteries run the sensor nodes those are used in WSN. The battery is impossible to charge or repair, so the most valuable resource for wireless sensor networks is power. Over the years, several strategies have been invented and used to preserve this precious WSN resource. One of the most successful approach for this purpose has turned out to be clustering. The aim of this paper is to suggest an effective technique for choosing cluster heads in WSNs to increase the lifetime of the network. To accomplish this task, Grey Wolf Optimizer (GWO) technique has been used. The general GWO was updated in this paper to meet the particular purpose of cluster head selection in WSNs. In this article, we have considered eleven attributes in the fitness function for the proposed algorithm. The simulation is carried out under different conditions. The results obtained show that the proposed protocol is superior in terms of energy consumption and network lifetime by evaluating the proposed protocol (i.e. CH-GWO protocol) with some well-existing cluster protocols. The suggested protocol forms energy-efficient and scalable clusters.

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

2019 ◽  
Vol 27 (04) ◽  
pp. 669-688 ◽  
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.

scholarly journals Approximate Multi-Degree Reduction of SG-Bézier Curves Using the Grey Wolf Optimizer Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1242
Author(s):  
Hu ◽  
Qiao ◽  
Qin ◽  
Wei
Keyword(s):  
Computer Aided Design ◽  
Fitness Function ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Degree Reduction ◽  
Bezier Curves ◽  
Bézier Curves ◽  
Geometric Properties ◽  
Reduction Effect ◽  
Aided Design

SG-Bézier curves have become a useful tool for shape design and geometric representation in computer aided design (CAD), owed to their good geometric properties, e.g., symmetry and convex hull property. Aiming at the problem of approximate degree reduction of SG-Bézier curves, a method is proposed to reduce the n-th SG-Bézier curves to m-th (m < n) SG-Bézier curves. Starting from the idea of grey wolf optimizer (GWO) and combining the geometric properties of SG-Bézier curves, this method converts the problem of multi-degree reduction of SG-Bézier curves into solving an optimization problem. By choosing the fitness function, the approximate multi-degree reduction of SG-Bézier curves with adjustable shape parameters is realized under unrestricted and corner interpolation constraints. At the same time, some concrete examples of degree reduction and its errors are given. The results show that this method not only achieves good degree reduction effect, but is also easy to implement and has high accuracy.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Qinyuan Huang ◽  
Qiang Li ◽  
Maoxia Ran ◽  
Xin Liu ◽  
Ying Zhou
Keyword(s):  
Defect Detection ◽  
Fitness Function ◽  
Grey Wolf Optimizer ◽  
Detection Accuracy ◽  
Grey Wolf ◽  
Internal Defect ◽  
Internal Defects ◽  
Minimum Value ◽  
Threshold Setting ◽  
Internal Defect Detection

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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