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Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) pp 65–74Cite as

A New Metaheuristic Bat-Inspired Algorithm

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 284))

Abstract

Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.

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Author information

Authors and Affiliations

  1. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK

    Xin-She Yang

Authors
  1. Xin-She Yang
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and A.I. E.T.S. Ingeniería Informática y de Telecomunicación , University of Granada, C/ Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    Juan R. González , David Alejandro Pelta  & Carlos Cruz ,  & 

  2. School of Computer Science , University of Nottingham, Jubilee Campus Wollaton Road, NG8 1BB, Nottingham, UK

    Germán Terrazas

  3. School of Computer Science, University of Nottingham , Jubilee Campus Wollaton Road, NG8 1BB, Nottingham, UK

    Natalio Krasnogor

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© 2010 Springer-Verlag Berlin Heidelberg

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Yang, XS. (2010). A New Metaheuristic Bat-Inspired Algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol 284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12538-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-12538-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12537-9

  • Online ISBN: 978-3-642-12538-6

  • eBook Packages: EngineeringEngineering (R0)

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