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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 4/2019

29.06.2017 | Original Article

Chaotic multi-verse optimizer-based feature selection

verfasst von: Ahmed A. Ewees, Mohamed Abd El Aziz, Aboul Ella Hassanien

Erschienen in: Neural Computing and Applications | Ausgabe 4/2019

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Abstract

The multi-verse optimizer (MVO) is a new evolutionary algorithm inspired by the concepts of multi-verse theory namely, the white/black holes, which represents the interaction between the universes. However, the MVO has some drawbacks, like any other evolutionary algorithms, such as slow convergence and getting stuck in local optima (maximum or minimum). This paper provides a novel chaotic MVO algorithm (CMVO) to avoid these drawbacks, where chaotic maps are used to improve the performance of MVO algorithm. The CMVO algorithm is applied to solve the feature selection problem, in which five benchmark datasets are used to evaluate the performance of CMVO algorithm. The results of CMVO is compared with standard MVO and two other swarm algorithms. The experimental results show that logistic chaotic map is the best chaotic map that increases the performance of MVO, and also the MVO is better than other swarm algorithms.
Vorheriger Artikel Rule extraction for fatty liver detection using neural networks
Nächster Artikel Distributed cooperative learning algorithms using wavelet neural network

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Metadaten
Titel
Chaotic multi-verse optimizer-based feature selection
verfasst von
Ahmed A. Ewees
Mohamed Abd El Aziz
Aboul Ella Hassanien
Publikationsdatum
29.06.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 4/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3131-4

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