of Meta-heuristic Optimization Approaches: in Virtue of Grey Wolf Optimization

Pol, Pooja; Pachghare, Vinod

doi:10.1109/gcat47503.2019.8978363

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Métodos meta-heurísticos são técnicas para problemas de otimizac ¸ão que se inspiram em fenômenos da natureza, como comportamentos de animais ou conceitos evolutivos [22], [23]. Em ciência da computac ¸ão e otimizac ¸ão matemática, essas técnicas são definidas como métodos de alto nível para encontrar, gerar ou selecionar uma heurística, que pode proporcionar resultados satisfatórios e otimizados [24].…”

Section: B Algoritmo Meta-heurísticounclassified

Algorithm for 5G Resource Management Optimization in Edge Computing

Lieira

Quessada

Cristiani

et al. 2021

IEEE Latin Am. Trans.

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Section: B Algoritmo Meta-heurísticounclassified

Algorithm for 5G Resource Management Optimization in Edge Computing

Lieira

Quessada

Cristiani

et al. 2021

IEEE Latin Am. Trans.

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Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm

Lieira

Quessada

Cristiani

et al. 2020

2020 IEEE Latin-American Conference on Communications (LATINCOM)

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Impact of I-GWO towards Trust based Resource Allocation in Collaborative Cloud

Pol¹,

Pachghare²

2022

ijngc

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The cloud computing is arising as a popular computing paradigm, as it is good in offering its users an on-demand scalable resource based services over the internet. In the peak hours, a single cloud is not at all efficient in serving an application; therefore the collaborative cloud model has been introduced. The collaborative cloud computing (CCC) make use of the globally-scattered distributed cloud resources of the diverse organizations collectively in a co-operative manner to provide the required service to the user. The allocation as well as the management of the resources is being a challenging task in the CCC, due to the heterogeneity of the resources. On the other hand, the assurance of the Quality of Service (QoS) and reliability of these resources is challenging. Further, it would be efficient if the resources are provided based on the system behavior. In this research work, a novel trust computing model is developed, which predicts both the QoS and Trust via analyzing the system behavior. The proposed model encloses three major phases: trust- QoS behavior estimation, resource matching and resource allocation. Initially, the QoS as well as Trust behavior of the system is estimated via a Neural Network (NN) model. Subsequently, the resource allocation is performed using the parallel resource matching framework, which is based on the concept of Map-Reduce. More particularly, the precious resource allocation is achieved by an optimization logic called Improved Grey Wolf Optimizer (IGWO). Here, the improvement of GWO emphasis the consideration of both the best and worst fitness.

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of Meta-heuristic Optimization Approaches: in Virtue of Grey Wolf Optimization

Cited by 3 publications

References 19 publications

Algorithm for 5G Resource Management Optimization in Edge Computing

Algorithm for 5G Resource Management Optimization in Edge Computing

Resource Allocation Technique for Edge Computing Using Grey Wolf Optimization Algorithm

Impact of I-GWO towards Trust based Resource Allocation in Collaborative Cloud

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