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An enhanced Bat algorithm with mutation operator for numerical optimization problems

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Abstract

This article introduces a new variation of a known metaheuristic method for solving global optimization problems. The proposed algorithm is based on the Bat algorithm (BA), which is inspired by the micro-bat echolocation phenomenon, and addresses the problems of local-optima trapping using a special mutation operator that enhances the diversity of the standard BA, hence the name enhanced Bat algorithm (EBat). The design of EBat is introduced and its performance is evaluated against 24 of the standard benchmark functions, and compared to that of the standard BA, as well as to several well-established metaheuristic techniques. We also analyze the impact of different parameters on the EBat algorithm and determine the best combination of parameter values in the context of numerical optimization. The obtained results show that the new EBat method is indeed a promising addition to the arsenal of metaheuristic algorithms and can outperform several existing ones, including the original BA algorithm.

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Acknowledgements

This research work was funded by Universiti Sains Malaysia under USM Fellowship 2016 [APEX (1002/JHEA/ATSG4001)] from Institute of Postgraduate Studies, UNIVERSITI SAINS MALAYSIA. The research was also partially supported by the Fundamental Research Grant Scheme (FRGS) for “Content-Based Analysis Framework for Better Email Forensic and Cyber Investigation” [203/PKOMP/6711426].

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Authors and Affiliations

  1. School of Computer Science, Universiti Sains Malaysia, Penang, Malaysia

    Waheed A. H. M. Ghanem & Aman Jantan

  2. Faculty of Education-Saber, University of Aden, Aden, Yemen

    Waheed A. H. M. Ghanem

  3. Faculty of Engineering, University of Aden, Aden, Yemen

    Waheed A. H. M. Ghanem

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  1. Waheed A. H. M. Ghanem
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  2. Aman Jantan
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Correspondence to Waheed A. H. M. Ghanem.

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Ghanem, W.A.H.M., Jantan, A. An enhanced Bat algorithm with mutation operator for numerical optimization problems. Neural Comput & Applic 31 (Suppl 1), 617–651 (2019). https://doi.org/10.1007/s00521-017-3021-9

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  • DOI: https://doi.org/10.1007/s00521-017-3021-9

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