Title: An enhanced firefly algorithm for function optimisation problems

Authors: Xiaoyu Lin; Yiwen Zhong; Hui Zhang

Addresses: College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China ' College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, China ' Pervasive Technology Institute, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202, USA

Abstract: An enhanced firefly algorithm (EFA) is proposed to improve the performance of the standard firefly algorithm (FA). After analysing the impacts of the attraction and the randomisation parameter in FA's movement, we introduce three improvement strategies. First, a virtual standard space is constructed to eliminate the negative influence generated by the long distance between two fireflies. Second, the randomisation parameter in FA's movement is replaced by a stochastic factor, which is integrated into FA's attractiveness to produce adaptive randomisation. Finally, a fine-grained evaluation strategy is applied to deal with the negative mutual influence among different dimensions. The experiments carried on classic and shifted benchmark functions show that the proposed EFA performs significantly better than standard FA in terms of both convergent speed and solution precision.

Keywords: firefly algorithm; virtual standard space; stochastic factor; adaptive randomness; fine-gained evaluation strategy; function optimisation; convergent speed; solution precision.

DOI: 10.1504/IJMIC.2013.052298

International Journal of Modelling, Identification and Control, 2013 Vol.18 No.2, pp.166 - 173

Published online: 31 Jul 2014 *

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