An improved Firefly Algorithm based Task Scheduling in Cloud Computing for Effective Resource Utilization

Krishnan, Vaishnav; Rao, Pinagadi Venkateswara; Raja, S. Daniel Madan; Bhargav, S.; Balaji, S.; Divya, V.

doi:10.1109/icaccs54159.2022.9785072

Cited by 3 publications

(2 citation statements)

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“…Researchers are increasingly merging threshold image segmentation with intelligent optimization algorithms as swarm intelligence optimization techniques grow over time. For instance, based on the enhanced Firefly algorithm [ 11 ], better particle swarm optimization algorithm [ 12 ], improved genetic algorithm [ 13 ], improved whale optimization algorithm [ 14 ], and improved cuckoo algorithm [ 15 ]. You can use these clever optimization methods to identify appropriate optimization objective functions.…”

Section: Introductionmentioning

confidence: 99%

Otsu Multi-Threshold Image Segmentation Based on Adaptive Double-Mutation Differential Evolution

Guo,

Wang,

Meng

et al. 2023

Biomimetics

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A quick and effective way of segmenting images is the Otsu threshold method. However, the complexity of time grows exponentially as the number of thresolds rises. The aim of this study is to address the issues with the standard threshold image segmentation method’s low segmentation effect and high time complexity. The two mutations differential evolution based on adaptive control parameters is presented, and the twofold mutation approach and adaptive control parameter search mechanism are used. Superior double-mutation differential evolution views Otsu threshold picture segmentation as an optimization issue, uses the maximum interclass variance technique as the objective function, determines the ideal threshold, and then implements multi-threshold image segmentation. The experimental findings demonstrate the robustness of the enhanced double-mutation differential evolution with adaptive control parameters. Compared to other benchmark algorithms, our algorithm excels in both image segmentation accuracy and time complexity, offering superior performance.

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Section: Introductionmentioning

confidence: 99%

Otsu Multi-Threshold Image Segmentation Based on Adaptive Double-Mutation Differential Evolution

Guo,

Wang,

Meng

et al. 2023

Biomimetics

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“…In (Manikandan et al, 2022) The authors in (Krishnan et al, 2022) introduced an improved Firefly Algorithm (FA)…”

Section: Chapter 3: Related Workmentioning

confidence: 99%

Particle Swarm Optimization with Enhanced Neighborhood Search Algorithm and Probabilistic Crossover Genetic Algorithm for Task Scheduling in Cloud Computing

Shamaa¹

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Considering the dynamic and elastic nature of cloud computing services, service providers must provide efficient task-scheduling solutions to accommodate the increasing demands in cloud services while satisfying Service Level Agreements (SLA) costeffectively.In this thesis, we present two novel task scheduling algorithms in cloud computing: ENS-PSO and PxGA, to minimize the makespan. ENS-PSO improves Particle Swarm Optimization (PSO) by introducing an effective neighborhood search technique. Also, we introduce static and dynamic methods to select ENS-PSO neighborhood search size. PxGA enhances the Genetic Algorithm (GA) by applying a weighted probabilistic approach to the crossover operation.CloudSim toolkit is utilized to evaluate the algorithms in terms of makespan, computational time, degree of imbalance, and energy consumption. The simulation results prove that ENS-PSO and PxGA outperform other classic and recent algorithms. Moreover, at the expense of higher computational time, ENS-PSO outperforms PxGA on the overall makespan by 3-4%.

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Probability-Based Crossover Genetic Algorithm for Task Scheduling in Cloud Computing

Shamaa

Shi

Ankenmann

2023

2023 6th Conference on Cloud and Internet of Things (CIoT)

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An improved Firefly Algorithm based Task Scheduling in Cloud Computing for Effective Resource Utilization

Cited by 3 publications

References 20 publications

Otsu Multi-Threshold Image Segmentation Based on Adaptive Double-Mutation Differential Evolution

Otsu Multi-Threshold Image Segmentation Based on Adaptive Double-Mutation Differential Evolution

Particle Swarm Optimization with Enhanced Neighborhood Search Algorithm and Probabilistic Crossover Genetic Algorithm for Task Scheduling in Cloud Computing

Probability-Based Crossover Genetic Algorithm for Task Scheduling in Cloud Computing

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