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A novel hybrid bat algorithm with a fast clustering-based hybridization

  • Sadegh EskandariEmail author
  • Mohammad Masoud Javidi
Research Paper
  • 13 Downloads

Abstract

Bat algorithm (BA) is a new and promising metaheuristic search algorithm which could outperform existing algorithms. However, BA can be easily trapped in a local optimum regarded to low exploration ability. The present study proposed a new local-search-based hybrid heuristic to escape such scenario. The proposed hybrid BA (hBA) uses a clustering-based hybridization method which detects the early convergence of BA population by analyzing similarities among individuals. The main motivation for such an analysis is that when BA is continually converging, the similarity among individuals becomes higher. The proposed hBA is extensively evaluated on CEC2017 benchmark suite. The Experiments demonstrate that the algorithm achieves better results than continues variants of BA in every way. Moreover, as a case study, a binary version of the proposed hBA (hBBA) is applied to the well-known feature selection problem. The recorded results on 13 datasets demonstrate that hBBA would be considered as a new state-of-art in metaheuristic-based wrapper feature selection methods.

Keywords

Bat Algorithm Metaheuristic search Nature inspired search Feature selection 

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Mathematical SciencesUniversity of GuilanRashtIran
  2. 2.Shahid Bahonar University of KermanKermanIran

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