Lévy flight artificial bee colony algorithm
Article
Sharma, H., Bansal, J., Arya, K. and Yang, X. 2016. Lévy flight artificial bee colony algorithm. International Journal of Systems Science. 47 (11), pp. 2652-2670. https://doi.org/10.1080/00207721.2015.1010748
Type | Article |
---|---|
Title | Lévy flight artificial bee colony algorithm |
Authors | Sharma, H., Bansal, J., Arya, K. and Yang, X. |
Abstract | Artificial bee colony (ABC) optimisation algorithm is a relatively simple and recent population-based probabilistic approach for global optimisation. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the ABC, there is a high chance to skip the true solution due to its large step sizes. In order to balance between diversity and convergence in the ABC, a Lévy flight inspired search strategy is proposed and integrated with ABC. The proposed strategy is named as Lévy Flight ABC (LFABC) has both the local and global search capability simultaneously and can be achieved by tuning the Lévy flight parameters and thus automatically tuning the step sizes. In the LFABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Furthermore, to improve the exploration capability, the numbers of scout bees are increased. The experiments on 20 test problems of different complexities and five real-world engineering optimisation problems show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest-guided ABC, best-so-far ABC and modified ABC in most of the experiments. |
Keywords | swarm intelligence; memetic algorithm; Levy flight local search; numerical optimisation |
Publisher | Taylor and Francis |
Journal | International Journal of Systems Science |
ISSN | 0020-7721 |
Electronic | 1464-5319 |
Publication dates | |
Online | 17 Mar 2015 |
17 Aug 2016 | |
Publication process dates | |
Deposited | 21 Apr 2016 |
Accepted | 21 Apr 2013 |
Submitted | 23 Jun 2012 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.1080/00207721.2015.1010748 |
Web of Science identifier | WOS:000372740400015 |
Language | English |
https://repository.mdx.ac.uk/item/86496
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