Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (20): 145-151.DOI: 10.3778/j.issn.1002-8331.1806-0036

Previous Articles     Next Articles

Fireworks algorithm with grey correlation operator

WANG Juqin1,2, SHI Yingzhong1,2, PENG Li3, JIANG Daoyin3   

  1. 1.School of Internet of Things, Wuxi Institute of Technology, Wuxi, Jiangsu 214121, China
    2.Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, Jiangsu 214122, China
    3.School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2018-10-15 Published:2018-10-19


汪菊琴1,2,史荧中1,2,彭  力3,姜道银3   

  1. 1.无锡职业技术学院 物联网技术学院,江苏 无锡 214121
    2.江南大学 江苏省媒体设计与软件技术重点实验室,江苏 无锡 214122
    3.江南大学 物联网工程学院,江苏 无锡 214122

Abstract: Due to the defect of the particle update mechanism in the fireworks algorithm, there is a lack of information exchange between particles in the optimization process, which makes the algorithm’s ability to search for optimization weak and the accuracy of search results to be low. In order to improve the defects, a fireworks algorithm with gray correlation operator(GFA) is proposed. GFA uses the grey correlation operator to select a guidance particle for each particle, and updates their own dimension information to improve the optimization ability. The improved algorithm is tested on standard functions. The results show that GFA has better optimization accuracy and convergence speed.

Key words: fireworks algorithm, information exchange, grey correlation operator, guiding particle

摘要: 标准烟花算法(FA)由于粒子更新机制的缺陷,寻优过程中粒子之间缺乏信息交流,使得算法寻优能力弱,寻优结果精度低。为了改善上述缺陷,提出带有灰色关联算子的烟花算法(GFA)。GFA在粒子寻优过程中利用灰色关联算子为每个粒子选取一个指导粒子,通过指导粒子来对自身粒子维度信息进行更新,以提高算法的寻优能力。利用标准测试函数对所提算法进行测试,仿真结果表明GFA与相关改进的烟花算法相比具有较高的寻优精度和收敛速度。

关键词: 烟花算法, 信息交流, 灰色关联算子, 指导粒子