A better exploration strategy in Grey Wolf Optimizer

Bansal, Jagdish Chand; Singh, Shitu

doi:10.1007/s12652-020-02153-1

Cited by 58 publications

(25 citation statements)

References 27 publications

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“…In [40], Singh and Bansal proposed a new grey wolf optimizer compound with crossover and oppositionbased learning named GWO-XOBL to overcome inadequate variety of wolves prone which led to local optima, and at the end, GWO performance will decrease, the new algorithm evaluated using 13 well-known standard benchmark problems to give expressively performance enhancement compared to GWO and other algorithms. In [41], Bansal and Singh proposed enhancement to some issues related to GWO algorithm, low exploration and slow convergence rate using explorative equation and opposition-based learning (OBL), where results show better effectiveness than other metaheuristic algorithms; 23 standard benchmark test problems are used to validate the proposed enhancement. In [42], a modified GWO (MGWO) was proposed to schedule jobs on virtual machines and enhance performance.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

Ababneh

2021

Mathematical Problems in Engineering

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In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

Ababneh

2021

Mathematical Problems in Engineering

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“…Grey wolf optimizer [41][42][43][44][45][46][47][48][49] is a new heuristic meta influenced via grey wolves; this illustrates the hierarchy chain of leadership and the grey wolf's hunting process. Based on four types of wolves, namely, alpha (𝛼), beta (𝛽), delta (𝛿), and omega (𝜔), the leadership hierarchy is proposed in a pack comprised of 5-12 wolves on average.…”

Section: Background Workmentioning

confidence: 99%

LBR‐GWO: Layered based routing approach using grey wolf optimization algorithm in wireless sensor networks

Dwivedi¹,

Patro²,

Srivastava³

et al. 2021

Concurrency and Computation

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The sensors deployed inside a wireless sensor network (WSN) have limited energy sources, significantly impacting the network throughput. This article's research objective is to increase network lifetime by developing an energy-efficient layered-based routing algorithm for WSNs using the grey wolf optimization (LBR-GWO) algorithm. In this, the grey wolf's leadership hierarchy has followed, which improves the network's energy capability. The entire region of the deployed nodes divides into four layers. In these nodes, layer one is chosen as cluster heads. If more than two nodes are present in layer one, then the cluster head is selected based on the game theory model; otherwise, the decision is made based on the node's residual energy. While the existing algorithm has several complex control parameter points, the current algorithm has fewer complex parameters. Therefore, in comparison to other algorithms, this algorithm is easy to apply in cluster-based sensor networks. Simulation findings prove the LBR-GWO algorithm supremacy for balancing energy consumption across the nodes and improving the network's lifetime compared to the LEACH, HEED, and PSO protocols.

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“…There are quad types of Grey wolves which are named as α , β , δ and ω hired for mimicking the leadership hierarchy 103 . All grey wolves in a pack strictly follow the social hierarchy, the layer of hierarchy decreases from α to ω search agents 104 . The top‐level in the ladder of Grey wolves is α.…”

Section: Meta‐heuristic Optimization Algorithmsmentioning

confidence: 99%

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

Arfeen

Sheikh

Azam

et al. 2020

Concurrency and Computation

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Microgrids have drawn substantial consideration due to high quality and reliable mix sources of electricity. This paper articulates the implication of innovative algorithms for cognitive microgrid. It perceived the algorithms that are backed by artificial intelligence (AI) are quite efficient due to the precision, convergence speed, and less computation time as compared to the conventional heuristic methods. Solar PV/Battery grid‐connected MG is modeled to achieve optimum size, supreme power quality, reduced fluctuations in voltage and frequency, reduced settling time, eliminate short transient currents, seamless power, least annual cost and high reliability as an objective function under wavering weather condition and dynamic load changes. Four broad categorizations of metaheuristic algorithms, that is, evolutionary, swarm intelligence, physics, and human intelligence‐based algorithms are well elaborated in this study. The optimal solution to the fitness function by using a hybrid optimization method also directed in the study. This paper gives deep insight to readers working in the area.

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A better exploration strategy in Grey Wolf Optimizer

Cited by 58 publications

References 27 publications

A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

LBR‐GWO: Layered based routing approach using grey wolf optimization algorithm in wireless sensor networks

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

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