A Novel Firefly Algorithm based on Improved Learning Mechanism

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/).

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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  -