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Neural Computing and Applications

, Volume 31, Issue 4, pp 991–1006 | Cite as

Chaotic multi-verse optimizer-based feature selection

  • Ahmed A. EweesEmail author
  • Mohamed Abd El Aziz
  • Aboul Ella Hassanien
Original Article

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.

Keywords

Multi-verse optimizer Chaotic maps Feature selection Dimensionality reduction 

Notes

Compliance with ethical standards

Conflict of interest

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

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Ahmed A. Ewees
    • 1
    Email author
  • Mohamed Abd El Aziz
    • 2
  • Aboul Ella Hassanien
    • 3
  1. 1.Department of ComputerDamietta UniversityDamiettaEgypt
  2. 2.Department of Mathematics, Faculty of ScienceZagazig UniversityZagazigEgypt
  3. 3.Faculty of Computers and InformationCairo UniversityGizaEgypt

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