Affiliations: [a] School of Electronics and Engineering, Naval University of Engineering, WuHan, China
| [b] School of Information Science and Technology, Jiujiang University, Jiujiang, China
Correspondence:
[*]
Corresponding author. Lianglin Cao, School of Electronics and Engineering, Naval University of Engineering, WuHan, 430033, China. E-mails: charlies_navy@aliyun.com and charlies@jju.edu.cn.
Abstract: Firefly algorithm (FA) is one of most important nature-inspired algorithm based on swarm intelligence. Meanwhile, FA uses the full attraction model, which results too many unnecessary movements and reduces the efficiency of searching the optimal solution. To overcome these problems, this paper presents a new job, how the better fireflies move, which is always ignored. The novel algorithm is called multiple swarm strategy firefly algorithm (MSFFA), in which multiple swarm attraction model and status adaptively switch approach are proposed. It is characterized by employing the multiple swarm attraction model, which not only improves the efficiency of searching the optimal solution, but also quickly finds the better fireflies that move in free status. In addition, the novel approach defines that the fireflies followed different rules in different status, and can adaptively switch the status of fireflies between the original status and the free status to balance the exploration and the exploitation. To verify the robustness of MSFFA, it is compared with other improved FA variants on CEC2013. In one case of 30 dimension on 28 test functions, the proposed algorithm is significantly better than FA, DFA, PaFA, MFA, NaFA,and NSRaFA on 24, 23, 23, 17, 15, and 24 functions, respectively. The experimental results prove that MSFFA has obvious advantages over other FA variants.
Keywords: Fiefly algorithm, multiple swarm strategy, adaptively switch, global
optimization
