A comprehensive review: Krill Herd algorithm (KH) and its applications

Bolaji, Asaju La’aro; Al-Betar, Mohammed Azmi; Awadallah, Mohammed A.; Khader, Ahamad Tajudin; Abualigah, Laith

doi:10.1016/j.asoc.2016.08.041

Cited by 163 publications

(56 citation statements)

References 27 publications

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“…As previously mentioned, metaheuristic-based approaches are conventionally classified into: evolutionary algorithm [27], [28], swarm intelligence [63], and trajectory algorithms [14]. In this paper, five metaheuristic algorithms are adopted to find the optimal WT parameters for the EEG signal denoising problem.…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

et al. 2020

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Background. The most common and successful technique for signal denoising with nonstationary signals, such as electroencephalogram (EEG) and electrocardiogram (ECG) is the wavelet transform (WT). The success of WT depends on the optimal configuration of its control parameters which are often experimentally set. Fortunately, the optimality of the combination of these parameters can be measured in advance by using the mean squared error (MSE) function. Method. In this paper, five powerful metaheuristic algorithms are proposed to find the optimal WT parameters for EEG signal denoising which are harmony search (HS), β-hill climbing (β-hc), particle swarm optimization (PSO), genetic algorithm (GA), and flower pollination algorithm (FPA). It is worth mentioning that this is the initial investigation of using optimization methods for WT parameter configuration. This paper then examines which efficient algorithm has obtained the minimum MSE and the best WT parameter configurations. Result. The performance of the proposed algorithms is tested using two standard EEG datasets, namely, Kiern's EEG dataset and EEG Motor Movement/Imagery dataset. The results of the proposed algorithms are evaluated using five common criteria: signal-to-noise-ratio (SNR), SNR improvement, mean square error (MSE), root mean square error (RMSE), and percentage root mean square difference (PRD). Interestingly, for almost all evaluating criteria, FPA achieves the best parameters configuration for WT and empowers this technique to efficiently denoise the EEG signals for almost all used datasets. To further validate the FPA results, a comparative study between the FPA results and the results of two previous studies is conducted, and the findings favor to FPA. Conclusion. In conclusion, the results show that the proposed methods for EEG signal denoising can produce better results than manual configurations based on ad hoc strategy. Therefore, using metaheuristic approaches to optimize the parameters for EEG signals positively affects the denoising process performance of the WT method.

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Section: Metaheuristic Algorithmsmentioning

confidence: 99%

EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

et al. 2020

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“…Each krill individual tries to keep a high density and close to the nearest food. The direction of the motion induced is derived from the local effect of each solution density, a target effect of the individuals density, and a repulsive individuals effect [12,15,28,26].…”

Section: The Basics Of Krill Herd Algorithmmentioning

confidence: 99%

A new hybridization strategy for krill herd algorithm and harmony search algorithm applied to improve the data clustering

Abualigah¹,

Khader²,

Al-Betar³

et al. 2017

Proceedings of the First EAI International Conference on Computer Science and Engineering

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Krill herd (KH) is a stochastic nature-inspired algorithm, it has been successfully used to solve many complex optimization problems. The performance of krill herd algorithm (KHA) is effected by poor exploitation capability. This paper proposes new data clustering algorithm based on a hybrid of krill herd algorithm (KHA) and harmony search (HS) algorithm (Harmony-KHA) in order to improve the data clustering technique. This hybrid strategy seeking to enhance the global search capability of the KHA. The enhancement includes of adding global search operator from HS algorithm for exploration around the optimal solution in KH and thus kill individuals move towards the global best solution. The proposed method is applied to preserve the best krill individual during the krill position update. Experiments were conducted using four standard datasets from the UCI Machine Learning Repository, which is used in the domain of data clustering. The results showed that the proposed hybrid KHA and HS algorithm (Harmony-KHA) is produced very accurate clusters, especially in the large dataset.

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“…We apply the feature selection technique based on PSO algorithm, which be-gins with random initial solutions and improves the population to reach the global optimal solution [4,22], which represent a new subset of features. Each unique feature in the given dataset considers as a dimension search space.…”

Section: Solution Representationmentioning

confidence: 99%

Unsupervised Text Feature Selection Technique Based on Particle Swarm Optimization Algorithm for Improving the Text Clustering

Abualigah¹,

Khader²,

Al-Betar³

et al. 2017

Proceedings of the First EAI International Conference on Computer Science and Engineering

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After incensing the amount of text information on internet web pages, the dealing with this information is very complex due to the volume of information. Text clustering technique is an appropriate task to deal with a huge amount of text documents by grouping set of documents into groups. Text documents contain uninformative features, which decrease the performance of the text clustering technique. Feature selection is an unsupervised technique used to select informative features by creating a new subset of informative features. This technique used to improve the performance of the underlying algorithm. Latterly, several complex optimization problems are success solved by metaheuristic al-gorithms. In this paper, we proposed the Particle swarm optimization algorithm to solve the feature selection problem, namely, (FSPSOTC). The feature selection technique encourages the k-mean text clustering technique to obtain more accurate clusters. Experiments were conducted using four standard benchmark text datasets with different characteris-tics. Experimental results showed that the proposed method (FSPSOTC) is enhanced the performance of the text clustering technique by dealing with a new subset of informative features.

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A comprehensive review: Krill Herd algorithm (KH) and its applications

Cited by 163 publications

References 27 publications

EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods

A new hybridization strategy for krill herd algorithm and harmony search algorithm applied to improve the data clustering

Unsupervised Text Feature Selection Technique Based on Particle Swarm Optimization Algorithm for Improving the Text Clustering

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