Binary whale optimisation: an effective swarm algorithm for feature selection

Eid, Heba F.

doi:10.1504/ijmheur.2018.10012912

Cited by 8 publications

(7 citation statements)

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“…Author and Reference Application Area Xu et al [51] Feature selection for network intrusion detection Abdel-Basset et al [69] single and multidimensional 0-1 knapsack problem Eid et al [71] feature selection for ten UCI datasets Hussein et al [72] Feature selection for twenty-four UCI datasets Agrawal et al [73] Feature selection for fourteen UCI datasets Hussein et al [74], [76] Feature selection for eleven UCI datasets Eid [75] Feature selection for nine UCI datasets Nadimi-Shahraki et al [77] Feature selection for medical datasets and COVID-19 Hussein et al [78] Travelling salesman problem, engineering problems (Tension/compression string, welded beam, pressure vessel)…”

Section: Table 3 Woa Binary Variants With Applicationsmentioning

confidence: 99%

A comprehensive survey of whale optimization algorithm: modifications and classification

Mahmood

Bawany

Tanweer

2023

IJEECS

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Whale optimization algorithm (WOA) is an emerging nature-inspired, swarm-intelligence based algorithm to solve optimization problems more efficiently. This algorithm is based on the bubble-net hunting strategy of the humpback whales. It has gained immense popularity among researchers, typically, due to its simple nature, fast convergence, and having minimum parameters. In the recent past, it has been widely adopted in various fields including data mining, machine learning, wireless sensor networks, cloud computing, civil engineering, and power systems due to its optimal performance. The WOA has given competitive results in comparison to the state-of-the-art optimization algorithms. In this study, we aim to present a comprehensive survey of WOA consisting of more than eighty existing variants of WOA. More specifically, we intend to put forward key aspects of WOA variants with reference to modifications and applications. Further, we classify the most dominant variants of WOA in distinct categories based on modification area such as equation modification, parameter tuning or the problem space for which an algorithm has been specifically altered. We believe that this study will be beneficial for the community working on optimization problems and it can serve as a basis for understanding the modification and improvement process of an optimization algorithm.

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Section: Table 3 Woa Binary Variants With Applicationsmentioning

confidence: 99%

A comprehensive survey of whale optimization algorithm: modifications and classification

Mahmood

Bawany

Tanweer

2023

IJEECS

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“…With the optimal wolves, mathematical equations may be used to formally reflect the hunting social team member's attitude to arrive at the best option [22]. The w wolves participate in the hunt procedure by following the other famous wolf members [20][21][22][23][24][25][26]. The following is a list of the main steps involved in hunting:…”

Section: Grey Wolf Optimizatimentioning

confidence: 99%

A Hybrid Multiwavelet Transform with Grey Wolf Optimization Used for an Efficient Classification of Documents

Salman,

Mahmoud Al-Jawher

2023

Int J Innov Comp

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In machine learning, feature selection is crucial to increase performance and shorten the model's learning time. It seeks to discover the pertinent predictors from high-dimensional feature space. However, a tremendous increase in the feature dimension space poses a severe obstacle to feature selection techniques. Study process to address this difficulty, the authors suggest a hybrid feature selection method consisting of the Multiwavelet transform and Gray Wolf optimization. The proposed approach minimizes the overall downsides while cherry picking the benefits of both directions. This notable wavelet transform development employs both wavelet and vector scaling functions. Additionally, multiwavelets have orthogonality, symmetry, compact support, and significant vanishing moments. One of the most advanced areas of study of artificial intelligence is optimization algorithms. Grey Wolf Optimization (GWO) here produced artificial techniques that yielded good performance results and were more responsive to current needs. Keywords — About four key words or phrases in order of importance, separated by commas, used to compile the subject index for the last issue for the year.

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“…It can be considered as a NP-hard combinatorial problem. Let us assume that the dataset D consists of d features, the possible feature subsets will be 2 d −1 [40]. Therefore, the binary seagull approaches are used to select the finest feature set.…”

Section: A Feature Selection In Data Mining Fieldmentioning

confidence: 99%

A Novel Binary Seagull Optimizer and its Application to Feature Selection Problem

Kumar

Kaur

et al. 2021

IEEE Access

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Seagull Optimization Algorithm (SOA) is a metaheuristic algorithm that mimics the migrating and hunting behaviour of seagulls. SOA is able to solve continuous real-life problems, but not to discrete problems. The eight different binary versions of SOA are proposed in this paper. The proposed algorithm uses four transfer functions, S-shaped and V-shaped, which are used to map the continuous search space into discrete search space. Twenty-five benchmark functions are used to validate the performance of the proposed algorithm. The statistical significance of the proposed algorithm is also analysed. Experimental results divulge that the proposed algorithm outperforms the competitive algorithms. The proposed algorithm is also applied on data mining. The results demonstrate the superiority of binary seagull optimization algorithm in data mining application.

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Binary whale optimisation: an effective swarm algorithm for feature selection

Cited by 8 publications

References 0 publications

A comprehensive survey of whale optimization algorithm: modifications and classification

A comprehensive survey of whale optimization algorithm: modifications and classification

A Hybrid Multiwavelet Transform with Grey Wolf Optimization Used for an Efficient Classification of Documents

A Novel Binary Seagull Optimizer and its Application to Feature Selection Problem

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