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Evolutionary Intelligence

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20.03.2021 | Research Paper

Chaotic vortex search algorithm: metaheuristic algorithm for feature selection

verfasst von: Farhad Soleimanian Gharehchopogh, Isa Maleki, Zahra Asheghi Dizaji

Erschienen in: Evolutionary Intelligence | Ausgabe 3/2022

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Abstract

The Vortex Search Algorithm (VSA) is a meta-heuristic algorithm that has been inspired by the vortex phenomenon proposed by Dogan and Olmez in 2015. Like other meta-heuristic algorithms, the VSA has a major problem: it can easily get stuck in local optimum solutions and provide solutions with a slow convergence rate and low accuracy. Thus, chaos theory has been added to the search process of VSA in order to speed up global convergence and gain better performance. In the proposed method, various chaotic maps have been considered for improving the VSA operators and helping to control both exploitation and exploration. The performance of this method was evaluated with 24 UCI standard datasets. In addition, it was evaluated as a Feature Selection (FS) method. The results of simulation showed that chaotic maps (particularly the Tent map) are able to enhance the performance of the VSA. Furthermore, it was clearly shown the fitness of the proposed method in attaining the optimal feature subset with utmost accuracy and the least number of features. If the number of features is equal to 36, the percentage of accuracy in VSA and the proposed model is 77.49 and 92.07. If the number of features is 80, the percentage of accuracy in VSA and the proposed model is 36.37 and 71.76. If the number of features is 3343, the percentage of accuracy in VSA and the proposed model is 95.48 and 99.70. Finally, the results on Real Application showed that the proposed method has higher percentage of accuracy in comparison to other algorithms.

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Titel: Chaotic vortex search algorithm: metaheuristic algorithm for feature selection
verfasst von: Farhad Soleimanian Gharehchopogh
Isa Maleki
Zahra Asheghi Dizaji
Publikationsdatum: 20.03.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Evolutionary Intelligence / Ausgabe 3/2022
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI: https://doi.org/10.1007/s12065-021-00590-1

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