Feature selection approach based on whale optimization algorithm

Sharawi, Marwa; Zawbaa, Hossam M.; Emary, Eid

doi:10.1109/icaci.2017.7974502

Cited by 83 publications

(50 citation statements)

References 9 publications

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“…Nakamura et al [13] conducted experiments to compare several meta-heuristic algorithms such as (BAT -FFA -PSO) but didn't include WOA in this comparison. Sharawi et al [14] introduced a feature selection approach using the WOA and proved that WOA has the ability to find the best features with maximum accuracy. Hassan et al [15] showed that a hybrid algorithm of WOA and Naïve Bayes (NB) saves storage space and accelerates the classification process.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning H₂O Framework for Emergency Prediction in Biomedical Big Data

et al. 2020

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Recently, the design and implementation of new healthcare systems have gained an interest in both industry and academia. The amalgamation between the Internet of Things, cloud, edge computing, and big data helps the proliferation of new scenarios for smart medical services and applications. Deep learning is currently paying a lot of attention for its utilization with big healthcare data. To this end, the main objective of this study is to propose a Deep Learning H 2 O (DLH 2 O) framework for improving the performance and selection of the optimal features to predict emergency cases. The proposed DLH 2 O framework consists of data preprocessing layer, feature selection layer and deep learning layer. The DLH 2 O framework aims to find the optimal subset of features and minimize the error of the classification through a proposed new variant of the Whale Optimization Algorithm (WOA) called ACP-WOA. The proposed changes have been done on the following parameters a, a2, A, and C which should affect both exploration and exploitation of WOA. The experiments conducted in order to test the validity of DLH 2 O Framework. In regard to the datasets, five experiments are performed for this purpose. The results demonstrate the superiority of ACP-WOA compared to the other state-of-the-art meta-heuristic algorithms in terms of time, error, and scalability. The proposed ACP-WOA is also tested on CEC2017 benchmark functions and proves its superiority over WOA in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning H₂O Framework for Emergency Prediction in Biomedical Big Data

et al. 2020

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“…Fig. 11(c), represents the inputs of DC-PI controller (V DCe ), output of DC-PI controller (V DCPI ), inphase unit template (u pa ), and signal obtained by (21), (v pa ). Similarly, Fig.…”

Section: A Performance Evaluation Of Aanf Based Control Algorithmmentioning

confidence: 99%

“…The idea of optimization technique has been adopted in the PI gains estimation for DVR control in [20]. A whale optimization algorithm (WOA) is a Meta heuristic optimization algorithm which uses the natural behavior of the humpback whales [21], [22]. WOA has been used in finding the best feature set with performance comparison with particle swarm optimization and genetic algorithm [21].…”

Section: Introductionmentioning

confidence: 99%

“…A whale optimization algorithm (WOA) is a Meta heuristic optimization algorithm which uses the natural behavior of the humpback whales [21], [22]. WOA has been used in finding the best feature set with performance comparison with particle swarm optimization and genetic algorithm [21]. An extensive study on WOA has been conducted by applying it in mathematical benchmark functions to analyze its potential for convergence and all other features of the optimization algorithm [22].…”

Section: Introductionmentioning

confidence: 99%

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Amplitude Adaptive Notch Filter With Optimized PI Gains for Mitigation of Voltage Based Power Quality Problems

Arya¹,

Maurya²,

Naidu³

2018

CPSS TPEA

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“…The experimental results show Symmetry 2018, 10, 210 3 of 31 that CWOA can effectively optimize the parameters of photovoltaic cells and their components. The literature [30] proposes a WOA-based feature selection approach, which is applied to find the largest subset of features so that it maximizes classification accuracy while retaining the minimum number of features.…”

Section: Introductionmentioning

confidence: 99%

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

2018

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Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc.). WOA's unique search mechanism enables it to have a strong global search capability while taking into account the strong global search capabilities. In this work, considering the the deficiency of WOA in local search mechanism, combined with the optimization methods of other group intelligent algorithms, perceptual perturbation mechanism is introduced, which makes the agent perform more detailed searches near the local extreme point. At the same time, since the WOA uses a logarithmic spiral curve, the agent cannot fully search all the spaces within its search range, even though the introduction of the perturbation mechanism may still lead to the algorithm falling into a local optimum. Therefore, the equal pitch Archimedes spiral curve is chosen to replace the classic logarithmic spiral curve. In order to fully verify the effect of the search path on the performance of the algorithm, several other spiral curves have been chosen for experimental comparison. By utilizing the 23 benchmark test functions, the simulation results show that WOA (PDWOA) with perceptual perturbation significantly outperforms the standard WOA. Then, based on the PDWOA, the effect of the search path on the performance of the algorithm has been verified. The simulation results show that the equal pitch of the Archimedean spiral curve is best.

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Feature selection approach based on whale optimization algorithm

Cited by 83 publications

References 9 publications

A Deep Learning H₂O Framework for Emergency Prediction in Biomedical Big Data

A Deep Learning H₂O Framework for Emergency Prediction in Biomedical Big Data

Amplitude Adaptive Notch Filter With Optimized PI Gains for Mitigation of Voltage Based Power Quality Problems

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

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Feature selection approach based on whale optimization algorithm

Cited by 83 publications

References 9 publications

A Deep Learning H2O Framework for Emergency Prediction in Biomedical Big Data

A Deep Learning H2O Framework for Emergency Prediction in Biomedical Big Data

Amplitude Adaptive Notch Filter With Optimized PI Gains for Mitigation of Voltage Based Power Quality Problems

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

Contact Info

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A Deep Learning H₂O Framework for Emergency Prediction in Biomedical Big Data

A Deep Learning H₂O Framework for Emergency Prediction in Biomedical Big Data