A multi-group firefly algorithm for numerical optimization

2nd Annual International Conference on Information System and Artificial Intelligence

物理学;计算机科学

Tong, Nan^1 ; Fu, Qiang^1,2 ; Zhong, Caiming^1 ; Wang, Pengjun^2

College of Science and Technology, Ningbo University, Ningbo

315212, China^1

Faculty of Information Science and Engineering, Ningbo University, Ningbo

315211, China^2

关键词: Evolution mechanism;    Evolution modeling;    Firefly algorithms;    Learning mechanism;    Local search;    Model parameters;    Numerical optimizations;    Pre-mature convergences;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012060/pdf
DOI  :  10.1088/1742-6596/887/1/012060

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

To solve the problem of premature convergence of firefly algorithm (FA), this paper analyzes the evolution mechanism of the algorithm, and proposes an improved Firefly algorithm based on modified evolution model and multi-group learning mechanism (IMGFA). A Firefly colony is divided into several subgroups with different model parameters. Within each subgroup, the optimal firefly is responsible for leading the others fireflies to implement the early global evolution, and establish the information mutual system among the fireflies. And then, each firefly achieves local search by following the brighter firefly in its neighbors. At the same time, learning mechanism among the best fireflies in various subgroups to exchange information can help the population to obtain global optimization goals more effectively. Experimental results verify the effectiveness of the proposed algorithm.