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Chaotic multi-verse optimizer-based feature selection

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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.

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Authors and Affiliations

  1. Department of Computer, Damietta University, Damietta, Egypt

    Ahmed A. Ewees

  2. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt

    Mohamed Abd El Aziz

  3. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Authors
  1. Ahmed A. Ewees
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  2. Mohamed Abd El Aziz
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  3. Aboul Ella Hassanien
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Correspondence to Ahmed A. Ewees.

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The authors state that there are no conflicts of interest, and this study was carried out without any funding sources.

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Ewees, A.A., El Aziz, M.A. & Hassanien, A.E. Chaotic multi-verse optimizer-based feature selection. Neural Comput & Applic 31, 991–1006 (2019). https://doi.org/10.1007/s00521-017-3131-4

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  • DOI: https://doi.org/10.1007/s00521-017-3131-4

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