Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism
Article
Wang, H., Cui, Z., Sun, H., Rahnamayan, S. and Yang, X. 2017. Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft Computing. 21 (18), pp. 5325-5339. https://doi.org/10.1007/s00500-016-2116-z
Type | Article |
---|---|
Title | Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism |
Authors | Wang, H., Cui, Z., Sun, H., Rahnamayan, S. and Yang, X. |
Abstract | Firefly algorithm (FA) is a new swarm intelligence optimization algorithm, which has shown an effective performance on many optimization problems. However, it may suffer from premature convergence when solving complex optimization problems. In this paper, we propose a new FA variant, called NSRaFA, which employs a random attraction model and three neighborhood search strategies to obtain a trade-off between exploration and exploitation abilities. Moreover, a dynamic parameter adjustment mechanism is used to automatically adjust the control parameters. Experiments are conducted on a set of well-known benchmark functions. Results show that our approach achieves much better solutions than the standard FA and five other recently proposed FA variants. |
Keywords | Firefly algorithm (FA); Random attraction; Neighborhood search; Dynamic parameter adjustment mechanism; Global optimization |
Publisher | Springer |
Journal | Soft Computing |
ISSN | 1432-7643 |
Electronic | 1433-7479 |
Publication dates | |
Online | 18 Mar 2016 |
Sep 2017 | |
Publication process dates | |
Deposited | 19 Apr 2016 |
Accepted | 02 Feb 2016 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00500-016-2116-z |
Web of Science identifier | WOS:000410259700012 |
Language | English |
https://repository.mdx.ac.uk/item/86423
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