计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 277-291.doi: 10.11896/jsjkx.220300269

• 人工智能 • 上一篇    下一篇

二进制哈里斯鹰优化及其特征选择算法

孙林, 李梦梦, 徐久成   

  1. 河南师范大学计算机与信息工程学院 河南 新乡 453007
    智慧商务与物联网技术河南省工程实验室 河南 新乡 453007
  • 收稿日期:2022-03-29 修回日期:2022-09-26 出版日期:2023-05-15 发布日期:2023-05-06
  • 通讯作者: 孙林(sunlin@htu.edu.cn)
  • 基金资助:
    国家自然科学基金(62076089,61976082,62002103,61901160);河南省科技攻关计划(212102210136,222102210169);河南省高等学校重点科研项目(22B520013)

Binary Harris Hawk Optimization and Its Feature Selection Algorithm

SUN Lin, LI Mengmeng, XU Jiucheng   

  1. College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China
    Engineering Lab of Intelligence Business and Internet of Things of Henan Province,Xinxiang,Henan 453007,China
  • Received:2022-03-29 Revised:2022-09-26 Online:2023-05-15 Published:2023-05-06
  • About author:SUN Lin,born in 1979,Ph.D,associate professor,master supervisor.His main research interests include granular computing,big data mining,machine lear-ning and bioinformatics.
  • Supported by:
    National Natural Science Foundation of China(62076089,61976082,62002103,61901160),Key Science and Technology Program of Henan Province,China(212102210136,222102210169) and Key Scientific Research Project of Henan Provincial Higher Education of China(22B520013).

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摘要： 针对哈里斯鹰优化(Harris Hawk Optimization,HHO)算法在探索阶段仅使用随机策略初始种群,致使种群多样性下降,控制开发和探索过程中的线性变化的逃逸能量,在迭代后期易陷入局部最优等问题,提出了二进制HHO及其元启发式特征选择算法。首先,在探索阶段引入Sine映射函数,初始化哈里斯鹰种群位置,运用自适应调整算子来改变HHO搜索范围,并更新HHO的种群位置。其次,利用对数惯性权重改进逃逸能量的更新公式,将迭代次数引入跳跃距离中,使用步长调整参数调整HHO的搜索距离,进而平衡探索与开发能力;在此基础上设计了改进的HHO算法,避免HHO算法陷入局部最优。然后,引入S型和V型传递函数,更新改进的HHO算法的二进制位置和种群位置,设计了两种二进制的改进HHO算法。最后,使用适应度函数评估特征子集,并将二进制改进HHO算法与适应度函数相结合,提出了两种基于二进制的改进HHO元启发式特征选择算法。在10个基准函数和17个公共数据集上的实验结果表明,4种优化策略在10个基准函数上有效提升了HHO算法的优化性能,改进的HHO算法明显优于对比的其他优化算法;在12个UCI数据集和5个高维基因数据集上,将所提算法与基于BHHO的特征选择算法和其他特征选择算法进行比较,实验结果显示,基于V型改进的HHO特征选择算法具备良好的寻优能力与分类性能。

关键词: 特征选择, 元启发式, 二进制, 哈里斯鹰优化, 适应度函数

Abstract: Harris Hawk optimization(HHO) algorithm only uses the random strategy to initialize the population in the exploration stage,which decreases the population diversity.The escape energy that controls the linear variation of the development and exploration process is prone to fall into local optimum in the later stage of iteration.To address the issues,this paper proposes a binary Harris Hawk optimization for metaheuristic feature selection algorithm.First,in the exploration phase,the Sine mapping function is introduced to initialize the population location of Harris Hawk,and the adaptive adjustment operator is used to change the search range of HHO and update the population location of HHO.Second,the updated formula of escape energy is improved by the logarithmic inertia weight,the number of iterations are introduced into the jump distance,and the step size adjustment parameter is employed to adjust the search distance of HHO to balance the exploration and development capabilities.On this basis,an improved HHO algorithm is designed to avoid the HHO algorithm falling into the local optimum.Third,the binary position and population position of the improved HHO algorithm are updated by the S-type and V-type transfer functions.Thus two binary improved HHO algorithms are designed.Finally,a fitness function is used to evaluate the feature subset,the binary improved HHO algorithm is combined with this fitness function,and then two binary improved HHO metaheuristic feature selection algorithms are developed.Experimental results on 10 benchmark functions and 17 public datasets show that the four optimization strategies effectively improve the optimization performance of the HHO algorithms on these benchmark functions,and the improved HHO algorithm is significantly better than other compared optimization algorithms.On 12 UCI datasets and 5 high-dimensional gene datasets,when compared with the BHHO-based feature selection algorithms and the other feature selection algorithms,the results demonstrate that the V-shape-based improved HHO feature selection algorithm has great optimization ability and classification performance.

Key words: Feature selection, Metaheuristic, Binary, Harris Hawk optimization, Fitness function

中图分类号: 

  • TP181
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