A Novel Firefly Algorithm based on Improved Learning Mechanism
Authors
Qiang Fu, Zheng Liu, Nan Tong, Mingbo Wang, Yiming Zhao
Corresponding Author
Qiang Fu
Available Online July 2015.
- DOI
- 10.2991/lemcs-15.2015.268How to use a DOI?
- Keywords
- Firefly algorithm; Distance weighting; Chaos; Gaussian mutation; Learning mechanism
- Abstract
According to the problem of low solution precision, slow convergence speed and some fireflies failure in the traditional firefly algorithm, an improved firefly optimization algorithm is proposed based on the learning mechanism, By chaotic maps, the Firefly's initial position in which dynamic distance weighting is introduced to strength the search ability of algorithm, and adaptive-step scheme is used to balance the dual requirement of local and global optimization. Gaussian mutation is also adopted in the algorithm to help firefly population jump out of local optimum effectively. Experimental results show that the proposed algorithm has rationality and effectiveness.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Qiang Fu AU - Zheng Liu AU - Nan Tong AU - Mingbo Wang AU - Yiming Zhao PY - 2015/07 DA - 2015/07 TI - A Novel Firefly Algorithm based on Improved Learning Mechanism BT - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science PB - Atlantis Press SP - 1343 EP - 1351 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.268 DO - 10.2991/lemcs-15.2015.268 ID - Fu2015/07 ER -