关键词: 动态搜索烟花算法, 爆炸半径, 变异算子, 学习因子

Abstract: Dynamic search fireworks algorithm (dynFWA) which adopts a dynamic explosion amplitude for core fireworks (CF) has been proved to be a great algorithm for solving optimization problems. However, dynFWA has the disadvantages that it is easy to fall into local optimal solutions prematurely and has slow convergence rate. In order to improve the above mentioned problems, this paper improves conventional dynFWA by embedding two different learning factors which make use of the history successful information, referred as improved dynamic search firework algorithm (IdynFWA). The learning factors take advantages of the information of the best firework in each generation, which makes the fireworks have the ability to learn from the excellent search. Moreover, two different generations of learning factor are beneficial to balance the local search and global search ability. The improved algorithm has been tested on  28 benchmark functions of CEC2013. And the experimental results show that IdynFWA significantly outperforms dynFWA, and achieves better performance than both SPSO2011 and DE/rand-to-best/1.

Key words: dynamic search fireworks algorithm, explosion amplitude, mutation operator, learning factor