RETRACTED ARTICLE: Hybrid Grey Wolf: Bald Eagle search optimized support vector regression for traffic flow forecasting

Angayarkanni, S. A.; Sivakumar, R.; Rao, Y. V. Ramana

doi:10.1007/s12652-020-02182-w

Cited by 43 publications

(23 citation statements)

References 28 publications

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“…This The bald eagle algorithm has more living nodes, at the first stage they high in the number of living nodes but round increases the alive node decreases on an existing the system, [ 11 ]. By using this methodology in a limited time to predict a solution for a problem and it can be proven by nature-inspired computation in a better time, energy, living node analysis, and distance [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm

Hasanin

Alsobhi

Khadidos

et al. 2021

Applied Bionics and Biomechanics

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Mobile edge computing (MEC) is a paradigm novel computing that promises the dramatic effect of reduction in latency and consumption of energy by computation offloading intensive; these tasks to the edge clouds in proximity close to the smart mobile users. In this research, reduce the offloading and latency between the edge computing and multiusers under the environment IoT application in 5G using bald eagle search optimization algorithm. The deep learning approach may consume high computational complexity and more time. In an edge computing system, devices can offload their computation-intensive tasks to the edge servers to save energy and shorten their latency. The bald eagle algorithm (BES) is the advanced optimization algorithm that resembles the strategy of eagle hunting. The strategies are select, search, and swooping stages. Previously, the BES algorithm is used to consume the energy and distance; to improve the better energy and reduce the offloading latency in this research and some delays occur when devices increase causes demand for cloud data, it can be improved by offering ROS (resource) estimation. To enhance the BES algorithm that introduces the ROS estimation stage to select the better ROSs, an edge system, which offloads the most appropriate IoT subtasks to edge servers then the expected time of execution, got minimized. Based on multiuser offloading, we proposed a bald eagle search optimization algorithm that can effectively reduce the end-end time to get fast and near-optimal IoT devices. The latency is reduced from the cloud to the local; this can be overcome by using edge computing, and deep learning expects faster and better results from the network. This can be proposed by BES algorithm technique that is better than other conventional methods that are compared on results to minimize the offloading latency. Then, the simulation is done to show the efficiency and stability by reducing the offloading latency.

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Section: Introductionmentioning

confidence: 99%

Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm

Hasanin

Alsobhi

Khadidos

et al. 2021

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

show abstract

“…The bald eagle search algorithm combines the desirable features of swarm intelligence (to locate the best position in the swarm) and evolutionary (expand search space to avoid local minima solutions) algorithm [ 48 ]. The BES algorithm is a recently introduced algorithm that mimics the intelligent social behavior of bald eagles in locating the best position for a food source (fish) [ 62 ]. The BES algorithm demonstrated good accuracy for benchmark problems and hence was chosen for this problem [ 48 ].…”

Section: Resultsmentioning

confidence: 99%

Analysis and Optimization of Dimensional Accuracy and Porosity of High Impact Polystyrene Material Printed by FDM Process: PSO, JAYA, Rao, and Bald Eagle Search Algorithms

et al. 2021

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High impact polystyrene (HIPS) material is widely used for low-strength structural applications. To ensure proper function, dimensional accuracy and porosity are at the forefront of industrial relevance. The dimensional accuracy cylindricity error (CE) and porosity of printed parts are influenced mainly by the control variables (layer thickness, shell thickness, infill density, print speed of the fused deposition modeling (FDM) process). In this study, a central composite design (CCD) matrix was used to perform experiments and analyze the complete insight information of the process (control variables influence on CE and porosity of FDM parts). Shell thickness for CE and infill density for porosity were identified as the most significant factors. Layer thickness interaction with shell thickness, infill density (except for CE), and print speed were found to be significant for both outputs. The interaction factors, i.e., shell thickness and infill density, were insignificant (negligible effect) for both outputs. The models developed produced a better fit for regression with an R2 equal to 94.56% for CE, and 99.10% for porosity, respectively. Four algorithms (bald eagle search optimization (BES), particle swarm optimization (PSO), RAO-3, and JAYA) were applied to determine optimal FDM conditions while examining six case studies (sets of weights assigned for porosity and CE) focused on minimizing both CE and porosity. BES and RAO-3 algorithms determined optimal conditions (layer thickness: 0.22 mm; shell thickness: 2 mm; infill density: 100%; print speed: 30 mm/s) at a reduced computation time equal to 0.007 s, differing from JAYA and PSO, which resulted in an experimental CE of 0.1215 mm and 2.5% of porosity in printed parts. Consequently, BES and RAO-3 algorithms are efficient tools for the optimization of FDM parts.

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“…al. 2020 ), Photovoltaic Systems ((Ramadan et al 2021 , Nicaire et al 2021 ), Parameter optimization of support vector machine (Angayarkanni et al 2021 ), Resource Management in Cloud Computing (Singh et al 2019 ), Parameter optimization of Neural Network (Xie et al 2021b ), Internet of Things (IoT) (Kapileswar and Phani Kumar 2022 ) Alsattar et. al.…”

Section: Survey On Recent Nature-inspired Optimization Algorithmsmentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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RETRACTED ARTICLE: Hybrid Grey Wolf: Bald Eagle search optimized support vector regression for traffic flow forecasting

Cited by 43 publications

References 28 publications

Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm

Efficient Multiuser Computation for Mobile-Edge Computing in IoT Application Using Optimization Algorithm

Analysis and Optimization of Dimensional Accuracy and Porosity of High Impact Polystyrene Material Printed by FDM Process: PSO, JAYA, Rao, and Bald Eagle Search Algorithms

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

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