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Slime Mould Algorithm: An Experimental Study of Nature-Inspired Optimiser

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Bioinspired Optimization Methods and Their Applications (BIOMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13627))

Abstract

In this paper, the performance of the slime mould algorithms (SMA) is studied. The original SMA algorithm is enhanced by several mechanisms to achieve better results in various problems. The Eigen transformation, linear reduction of population size, and perturbation of the solution are proposed and combined together with various settings of control parameters. All 16 newly proposed variants of SMA are compared with the original SMA and 16 various nature-inspired methods. All the algorithms are applied to 22 real-world problems called CEC 2011. Achieved results illustrate the good performance of the newly proposed SMA variants, especially compared with the original SMA algorithm.

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Authors and Affiliations

  1. University of Ostrava, 30. dubna 22, 70200, Ostrava, Czech Republic

    Petr Bujok & Martin Lacko

Authors
  1. Petr Bujok
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  2. Martin Lacko
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Correspondence to Petr Bujok .

Editor information

Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Marjan Mernik

  2. Jožef Stefan Institute, Ljubljana, Slovenia

    Tome Eftimov

  3. University of Maribor, Maribor, Slovenia

    Matej Črepinšek

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Bujok, P., Lacko, M. (2022). Slime Mould Algorithm: An Experimental Study of Nature-Inspired Optimiser. In: Mernik, M., Eftimov, T., Črepinšek, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2022. Lecture Notes in Computer Science, vol 13627. Springer, Cham. https://doi.org/10.1007/978-3-031-21094-5_15

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  • DOI: https://doi.org/10.1007/978-3-031-21094-5_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21093-8

  • Online ISBN: 978-3-031-21094-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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