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Review of Parameter Tuning Methods for Nature-Inspired Algorithms

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Benchmarks and Hybrid Algorithms in Optimization and Applications

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can largely influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ensure the algorithm used for optimization may perform well and can be sufficiently robust for solving different types of optimization problems. This chapter reviews some of the main methods for parameter tuning and then highlights the important issues concerning the latest development in parameter tuning. A few open problems are also discussed with some recommendations for future research.

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Author information

Authors and Affiliations

  1. School of Science and Technology, Middlesex University London, The Burroughs, London, NW4 4BT, UK

    Geethu Joy, Christian Huyck & Xin-She Yang

  2. Also Computer Engineering and Informatics, Middlesex University Dubai, Dubai Knowledge Park, P.O. Box 500697, Dubai, United Arab Emirates

    Geethu Joy

Authors
  1. Geethu Joy
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  2. Christian Huyck
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  3. Xin-She Yang
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Corresponding author

Correspondence to Xin-She Yang .

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Editors and Affiliations

  1. Department of Design Engineering and Mathematics, Middlesex University, London, UK

    Xin-She Yang

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Joy, G., Huyck, C., Yang, XS. (2023). Review of Parameter Tuning Methods for Nature-Inspired Algorithms. In: Yang, XS. (eds) Benchmarks and Hybrid Algorithms in Optimization and Applications. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-99-3970-1_3

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