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Journal of Applied MathematicsVolume 2013 2013, Article ID 832718, 8 pages

Research Article

Institute of Computer Network Systems, Hefei University of Technology, Hefei 230009, China

School of Information Engineering, West Anhui University, Lu-an 237012, China

Received 1 July 2013; Accepted 17 September 2013

Academic Editor: Sabri Arik

Copyright © 2013 Shuhao Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the standard firefly algorithm, each firefly has the same step settings and its values decrease from iteration to iteration. Therefore, it may fall into the local optimum. Furthermore, the decreasing of step is restrained by the maximum of iteration, which has an influence on the convergence speed and precision. In order to avoid falling into the local optimum and reduce the impact of the maximum of iteration, a self-adaptive step firefly algorithm is proposed in the paper. Its core idea is setting the step of each firefly varying with the iteration, according to each firefly’s historical information and current situation. Experiments are made to show the performance of our approach compared with the standard FA, based on sixteen standard testing benchmark functions. The results reveal that our method can prevent the premature convergence and improve the convergence speed and accurateness.

Author: Shuhao Yu, Shanlin Yang, and Shoubao Su



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