Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (9): 2668-2677.DOI: 10.11772/j.issn.1001-9081.2020111776

Special Issue: 先进计算

• Advanced computing • Previous Articles     Next Articles

Improved butterfly optimization algorithm based on cosine similarity

CHEN Jun, HE Qing   

  1. College of Big Data and Information Engineering, Guizhou University, Guiyang Guizhou 550025, China
  • Received:2020-11-10 Revised:2021-02-03 Online:2021-09-10 Published:2021-05-12
  • Supported by:
    This work is partially supported by the Major Special Project of Guizhou Provincial Science and Technology Program (Qiankehe Major Special Project[2018]3002, Qiankehe Major Special Project[2016]3022), the Youth Science and Technology Talent Growth Project of Guizhou Provincial Department of Education (Qiankehe KY[2016]124), the Guizhou University Cultivation Project (Qiankehe Platform Talent[2017]5788), the Open Project of Guizhou Provincial Key Laboratory of Public Big Data (2017BDKFJJ004).

基于余弦相似度的改进蝴蝶优化算法

陈俊, 何庆   

  1. 贵州大学 大数据与信息工程学院, 贵阳 550025
  • 通讯作者: 何庆
  • 作者简介:陈俊(1996-),男,贵州毕节人,硕士研究生,主要研究方向:进化计算、自然语言处理;何庆(1982-),男(苗族),贵州贵阳人,副教授,博士,主要研究方向:进化计算、认知无线电。
  • 基金资助:
    贵州省科技计划项目重大专项(黔科合重大专项字[2018]3002,黔科合重大专项字[2016]3022);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);贵州省公共大数据重点实验室开放课题(2017BDKFJJ004)。

Abstract: Aiming at the problems that Butterfly Optimization Algorithm (BOA) tends to fall into local optimum and has poor convergence, a Multi-Strategy Improved BOA (MSBOA) was proposed. Firstly, the cosine similarity position adjustment strategy was introduced to the algorithm, rotation transformation operator and scaling transformation operator were used to update the positions, so as to effectively maintain the population diversity of the algorithm. Secondly, dynamic switching probability was introduced to balance the transformation between the local phase and the global phase of the algorithm. Finally, a hybrid inertia weight strategy was added to accelerate convergence. Solving 16 benchmark test functions, as well as the Wilcoxon rank-sum test and CEC2014 test functions were to verify, the effectiveness and robustness of the proposed algorithm. Experimental results show that compared with BOA, some BOAs with different improvement strategies and some swarm intelligence algorithms, MSBOA has significant improvement in convergence accuracy and convergence speed.

Key words: cosine similarity, Butterfly Optimization Algorithm (BOA), rotation transformation operator, scaling transformation operator, adaptive inertia weight

摘要: 针对蝴蝶优化算法(BOA)容易陷入局部最优和收敛性差等问题,提出一种多策略改进的蝴蝶优化算法(MSBOA)。首先引入余弦相似度位置调整策略,通过旋转变化算子和伸缩变换算子进行位置更新,从而有效地保持BOA的种群多样性;其次引入动态切换概率,来平衡BOA局部阶段和全局阶段的转换;最后增加混合惯性权重策略,以提高BOA的收敛速度。使用16个基准测试函数、Wilcoxon检验以及部分CEC2014函数来验证MSBOA的有效性和鲁棒性。仿真实验结果表明,与BOA和其他改进策略BOA及其他群智能算法相比,MSBOA在收敛精度和收敛速度上有明显的提升。

关键词: 余弦相似度, 蝴蝶优化算法, 旋转变换算子, 伸缩变换算子, 自适应惯性权重

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