Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (7): 1661-1672.DOI: 10.3778/j.issn.1673-9418.2012066

• Theory and Algorithm • Previous Articles     Next Articles

Butterfly Optimization Algorithm for Chaotic Feedback Sharing and Group Synergy

LI Shouyu1, HE Qing1,+(), DU Nisuo2   

  1. 1. College of Big Data & Information Engineering, Guizhou University, Guiyang 550025, China
    2. Guizhou Big Data Academy, Guizhou University, Guiyang 550025, China
  • Received:2020-11-30 Revised:2021-01-25 Online:2022-07-01 Published:2021-01-28
  • Supported by:
    the Science and Technology Planning Project Major Special Project of Guizhou Province(黔科合重大专项字[2018]3002);the Science and Technology Planning Project Major Special Project of Guizhou Province(黔科合重大专项字[2016]3022);the Open Project of Guizhou Key Laboratory of Public Big Data(2017BDKFJJ004);the Youth Science and Technology Talent Growth Project of Guizhou Provincial Department of Education(黔科合KY 字[2016]124);the Guizhou University Cultivation Project(黔科合平台人才[2017]5788)

混沌反馈共享和群体协同效应的蝴蝶优化算法

李守玉1, 何庆1,+(), 杜逆索2   

  1. 1.贵州大学 大数据与信息工程学院,贵阳 550025
    2.贵州大学 贵州省大数据产业发展应用研究院,贵阳 550025
  • 作者简介:李守玉(1996—),男,贵州安顺人,硕士研究生,主要研究方向为进化计算、深度学习。
    LI Shouyu, born in 1996, M.S. candidate. His research interests include evolutionary computation and deep learning.
    何庆(1982—),男,贵州黔南州人,博士,副教授,主要研究方向为大数据应用、进化计算。
    HE Qing, born in 1982, Ph.D., associate professor. His research interests include big data application and evolutionary computation.
    杜逆索(1986—),男,贵州六盘水人,博士研究生,讲师,主要研究方向为深度学习。
    DU Nisuo, born in 1986, Ph.D. candidate, lecturer. His research interest is deep learning.
  • 基金资助:
    贵州省科技计划项目重大专项项目(黔科合重大专项字[2018]3002);贵州省科技计划项目重大专项项目(黔科合重大专项字[2016]3022);贵州省公共大数据重点实验室开放课题(2017BDKFJJ004);贵州省教育厅青年科技人才成长项目(黔科合KY 字[2016]124);贵州大学培育项目(黔科合平台人才[2017]5788)

Abstract:

A butterfly optimization algorithm (BOA) based on chaotic feedback sharing and group synergy (CFSBOA) is proposed to solve the shortcomings of low precision and easy to fall into local optimum. Firstly, using Hénon chaos to initialize the population can make the population cover the search blind area as much as possible, increase the diversity of the population, and improve the ability of optimizing the algorithm. Secondly, using the ideas of positive and negative feedback mechanism in feedback control circuit, it builds butterfly feedback shared communication network, allowing individuals to receive information from multiple directions to help populations of positioning the location of the optimal solution and perform careful search, enhance the ability to escape from local optimum and accelerate the algorithm convergence speed. Finally, the collective synergistic effect mechanism is used to improve and balance the global and local search ability and enhance the global and local optimization ability of the algorithm. The performance of the improved butterfly optimization algorithm is verified by using different dimension benchmark test functions, statistical test, Wilcoxon test and multiple types of CEC2014 partial functions. Compared with the new improved butterfly algorithm and other swarm intelligence algorithms, the experimental results show that the proposed algorithm has obvious advantages.

Key words: butterfly optimization algorithm (BOA), chaotic mapping, feedback sharing, group synergy

摘要:

针对蝴蝶优化算法(BOA)寻优精度低和易陷入局部最优等缺点,提出了混沌反馈共享和群体协同效应的蝴蝶优化算法(CFSBOA)。首先,利用Hénon混沌初始化种群,能够使种群尽可能地覆盖搜索盲区,增加种群多样性,提高算法寻优性能;其次,利用反馈控制电路中正负反馈作用机制的思想,构建蝴蝶之间的反馈共享交流网络,使得蝴蝶个体能够接收来自多个方向的信息,帮助种群定位最优解的位置并执行精细搜索,增强算法逃离局部最优的能力和加快算法收敛的速度;最后,利用群体协同效应机制,提高和平衡全局与局部搜索的能力,增强算法的全局和局部的寻优能力。使用不同维度的基准测试函数、统计检验、Wilcoxon检验及多类型的CEC2014部分函数验证改进蝴蝶优化算法的性能,并与新改进的蝴蝶算法及其他群智能算法进行对比,实验结果表明该算法具有明显优势。

关键词: 蝴蝶优化算法(BOA), 混沌映射, 反馈共享, 群体协同效应

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