Attraction and diffusion in nature-inspired optimization algorithms
Article
Yang, X., Deb, S., Hanne, T. and He, X. 2015. Attraction and diffusion in nature-inspired optimization algorithms. Neural Computing and Applications. https://doi.org/10.1007/s00521-015-1925-9
Type | Article |
---|---|
Title | Attraction and diffusion in nature-inspired optimization algorithms |
Authors | Yang, X., Deb, S., Hanne, T. and He, X. |
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. |
Publisher | Springer |
Journal | Neural Computing and Applications |
ISSN | 0941-0643 |
Publication dates | |
15 May 2015 | |
Publication process dates | |
Deposited | 14 Apr 2016 |
Accepted | 06 May 2015 |
Accepted author manuscript | |
Copyright Statement | The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-015-1925-9 |
Additional information | First online: 15 May 2015 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00521-015-1925-9 |
Language | English |
https://repository.mdx.ac.uk/item/863vx
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