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Attraction and diffusion in nature-inspired optimization algorithms

  • Theory and Applications of Soft Computing Methods
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Abstract

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behavior and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms and then point out some key topics for further research.

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Authors and Affiliations

  1. School of Science and Technology, Middlesex University, Hendon Campus, London, NW4 4BT, UK

    Xin-She Yang

  2. Cambridge Institute of Technology, Cambridge Village, Tatisilwai, Ranchi, 835103, Jharkhand, India

    Suash Deb

  3. University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstr. 16, 4600, Olten, Switzerland

    Thomas Hanne

  4. School of Science, Xi’an Polytechnic University, No. 19 Jinhua South Road, Xi’an, China

    Xingshi He

Authors
  1. Xin-She Yang
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  2. Suash Deb
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  3. Thomas Hanne
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  4. Xingshi He
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Correspondence to Thomas Hanne.

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Yang, XS., Deb, S., Hanne, T. et al. Attraction and diffusion in nature-inspired optimization algorithms. Neural Comput & Applic 31, 1987–1994 (2019). https://doi.org/10.1007/s00521-015-1925-9

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  • DOI: https://doi.org/10.1007/s00521-015-1925-9

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