Attraction and diffusion in nature-inspired optimization algorithms

Yang, Xin-She ORCID: https://orcid.org/0000-0001-8231-5556, Deb, Suash, Hanne, Thomas and He, Xingshi (2015) Attraction and diffusion in nature-inspired optimization algorithms. Neural Computing and Applications . ISSN 0941-0643 (Published online first) (doi:https://doi.org/10.1007/s00521-015-1925-9)

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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 behaviour 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.

Item Type: Article
Additional Information: First online: 15 May 2015
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 19276
Notes on copyright: The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-015-1925-9
Useful Links:
Depositing User: Xin-She Yang
Date Deposited: 14 Apr 2016 12:41
Last Modified: 11 Jun 2019 19:45
URI: https://eprints.mdx.ac.uk/id/eprint/19276

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