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Optimization of Benchmark Mathematical Functions Using the Firefly Algorithm

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Recent Advances on Hybrid Approaches for Designing Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 547))

Abstract

Nature-inspired algorithms are more relevant today, such as PSO and ACO, which have been used in various types of problems such as the optimization of neural networks, fuzzy systems, control, and others showing good results. There are other methods that have been proposed more recently, the firefly algorithm is one of them, this paper will explain the algorithm and describe how it behaves. In this chapter the firefly algorithm was applied in optimizing benchmark functions and comparing the results of the same functions with genetic algorithms.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Cinthya Solano-Aragón & Oscar Castillo

Authors
  1. Cinthya Solano-Aragón
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  2. Oscar Castillo
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Corresponding author

Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Solano-Aragón, C., Castillo, O. (2014). Optimization of Benchmark Mathematical Functions Using the Firefly Algorithm. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-05170-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05169-7

  • Online ISBN: 978-3-319-05170-3

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