Improved Harris Hawks Algorithm and Its Application in Feature Selection  

作　　者：Qianqian Zhang Yingmei Li Jianjun Zhan Shan Chen 

机构地区：[1]School of Computer Science and Information Engineering,Harbin Normal University,Harbin,150025,China [2]College of Systems Engineering,National University of Defense Technology,Changsha,410073,China

出　　处：《Computers, Materials & Continua》2024年第10期1251-1273,共23页计算机、材料和连续体（英文）

基　　金：supported by the National Natural Science Foundation of China(grant number 62073330);constituted a segment of a project associated with the School of Computer Science and Information Engineering at Harbin Normal University。

摘　　要：This research focuses on improving the Harris’Hawks Optimization algorithm(HHO)by tackling several of its shortcomings,including insufficient population diversity,an imbalance in exploration vs.exploitation,and a lack of thorough exploitation depth.To tackle these shortcomings,it proposes enhancements from three distinct perspectives:an initialization technique for populations grounded in opposition-based learning,a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration,and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators.The effectiveness of the Improved Harris Hawks Optimization algorithm(IHHO)is assessed by comparing it to five leading algorithms across 23 benchmark test functions.Experimental findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving capabilities.Additionally,this paper introduces a feature selection method leveraging the IHHO algorithm(IHHO-FS)to address challenges such as low efficiency in feature selection and high computational costs(time to find the optimal feature combination and model response time)associated with high-dimensional datasets.Comparative analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight datasets.The results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality,while also enhancing the efficiency of feature selection.Furthermore,IHHO-FS shows strong competitiveness relative to numerous algorithms.

关 键 词：HHO IHHO population diversity energy factor update strategy deep exploitation strategy feature selection 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]