A Novel Hybrid Firefly Algorithm Based on the Vector Angle Learning Mechanism

Abstract

The firefly algorithm (FA) is one of the swarm intelligence algorithms which can solve global optimization problems accurately. In the traditional FA, the position of each firefly can only be updated by the brightness of other fireflies around it. As a result, it is simple to update the firefly position but easy to fall into local optimum. In this paper, a novel hybrid firefly algorithm based on the vector angle learning mechanism (HFA-VAL) is proposed, which can combine the advantages of both the firefly algorithm (FA) and differential evolution (DE) by the vector angle learning mechanism. HFA-VAL employs vector angle parameters to adaptively adjust the moving step length of firefly in order to avoid falling into local optimum. In the evolutionary process, the difference method is used to update the dominant leader, so as to improve the moving direction of other fireflies and expand the search ability. In order to understand the strengths and weaknesses of HFA-VAL, several experiments are carried out on 25 benchmark functions in CEC2005. Experimental results show that the performance of HFA-VAL algorithm is better than other the-state-of-art algorithms.INDEX TERMS Vector angle learning mechanism, hybrid firefly algorithm, differential evolution, logarithmic spiral parameter.

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“…Chao et al [7] propose a linear decreasing inertia weight strategy and applied it to the WOA. Xu et al [8] combine differential evolution (DE) and FA, and adjust the firefly's moving step adaptively by using vector angle parameters to avoid local optimum. DE is used to change the moving direction of FA and enhance exploration ability.…”