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Distributed Grey Wolf Optimizer for scheduling of workflow applications in cloud environments
Applied Soft Computing, 2021Optimal scheduling of workflows in cloud computing environments is an essential element to maximize the utilization of Virtual Machines (VMs). In practice, scheduling of dependent tasks in a workflow requires distributing the tasks to the available VMs ...
Bilal H. Abed-alguni, N. Alawad
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Entropy Based Grey Wolf Optimizer
2020Recently Shannon’s Entropy has been incorporated in nature inspired metaheuristics with good results. Depending on the problem, the Grey Wolf Optimization (GWO) algorithm may suffer from premature convergence. Here, an Entropy Grey Wolf Optimization (E-GWO) technique is proposed with the overall aim to improve the original GWO performance.
Daniel Duarte +2 more
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Chaotic grey wolf optimization
2016 International Conference on Progress in Informatics and Computing (PIC), 2016Grey wolf optimization algorithm (GWO) is a recently proposed meta-heuristics and has shown promising performance in solving complex function optimization and engineering problems. To further enrich the search dynamics of GWO, the chaotic local search (CLS) mechanism is incorporated into GWO to enhance the search by taking the properties of ergodicity ...
Hang Yu +4 more
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A chaotic grey wolf optimizer for constrained optimization problems
Expert Systems, 2021AbstractBio‐inspired algorithms have become popular due to their capability of finding good solutions for complex optimization problems in an acceptable computational time. The Grey Wolf Optimizer is a nature‐inspired, population‐based metaheuristic that simulates the social hierarchy and the hunting strategy observed in a grey wolf pack.
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Transportation letters, 2022
Precise charging time prediction can effectively mitigate the inconvenience to drivers induced by inevitable charging behavior throughout trips. Although the effectiveness of the machine learning (ML) algorithm in predicting future outcomes has been ...
Irfan Ullah +4 more
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Precise charging time prediction can effectively mitigate the inconvenience to drivers induced by inevitable charging behavior throughout trips. Although the effectiveness of the machine learning (ML) algorithm in predicting future outcomes has been ...
Irfan Ullah +4 more
semanticscholar +1 more source
Empirical Study of Grey Wolf Optimizer
2016In this paper, the authors empirically investigate performance of the grey wolf optimizer (GWO). A test suite of six non-linear benchmark functions, well studied in the swarm and the evolutionary optimization literature, is selected to highlight the findings. The test suite contains three unimodal and three multimodal functions.
Avadh Kishor, Pramod Kumar Singh
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Multiobjective Design of 2D Hyperchaotic System Using Leader Pareto Grey Wolf Optimizer
IEEE Transactions on Systems, Man, and Cybernetics: SystemsA chaotic system is a mathematical model exhibiting random and unpredictable behavior. However, existing chaotic systems suffer from suboptimal parameters regarding chaotic indicators.
A. Toktaş +3 more
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Discrete Grey Wolf Optimizer for symmetric travelling salesman problem
Applied Soft Computing, 2021Grey Wolf Optimizer (GWO) is a recently developed population-based metaheuristic algorithm which imitates the behaviour of grey wolves for survival. Initially, GWO was proposed to solve continuous optimization problems where it performed well.
Karuna Panwar, Kusum Deep
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An improved hybrid grey wolf optimization algorithm
Soft Computing, 2018The existing grey wolf optimization algorithm has some disadvantages, such as slow convergence speed, low precision and so on. So this paper proposes a grey wolf optimization algorithm combined with particle swarm optimization (PSO_GWO). In this new algorithm, the Tent chaotic sequence is used to initiate the individuals’ position, which can increase ...
Zhi-jun Teng, Jin-ling Lv, Li-wen Guo
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A better exploration strategy in Grey Wolf Optimizer
Journal of Ambient Intelligence and Humanized Computing, 2020The Grey Wolf Optimizer (GWO) is a recently developed population-based meta-heuristics algorithm that mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Although, GWO has shown very good results on several real-life applications but still it suffers from some issues like, the low exploration and slow convergence rate ...
Jagdish Chand Bansal, Shitu Singh
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