现代电子技术2024，Vol.47Issue(18)：156-162,7.DOI:10.16652/j.issn.1004-373x.2024.18.026
一种基于改进差分进化的K-Means聚类算法研究
Research on K-Means clustering algorithm based on AGDE
摘要
Abstract
In order to improve the instability and low efficiency of clustering results caused by randomly selecting initial cluster centers in traditional K-Means clustering algorithms,a K-Means clustering algorithm based on adaptive guided differential evolution(AGDE-KM)is proposed.The adaptive operation operator is designed to improve the global search capability of the algorithm in the early stage and the convergence speed in the later stage.The multi-variation strategy is designed and the weight coefficient is introduced to play the advantages of different variation strategies in different evolutionary stages of the algorithm,balance the global and local search ability of the algorithm,and accelerate the convergence speed of the algorithm.A Gaussian perturbation crossover operation based on the best individual of the current population is proposed to provide a better evolutionary direction for the individual while maintaining the population diversity in"dimension",so as to avoid the algorithm from falling into local optimal.The optimal solution when the algorithm stops execution is used as the initial cluster center to replace the randomly selected cluster center of traditional K-Means.The comparative experiment of the proposed algorithm on the Vowel,Iris,and Glass datasets and the synthetic dataset Jcdx in the UCI public database are conducted.The results show that,in comparison with traditional K-Means,the sum of squared errors(SSE)is reduced by 5.65%,19.59%,13.31%,and 6.1%,respectively,and the clustering time is reduced by 83.03%,76.48%,77.47%,and 92.63%,respectively.The experimental results show that the proposed improved algorithm has faster convergence speed and better optimization finding ability,which can significantly improve the effectiveness,efficiency and stability of clustering.关键词
K-Means聚类算法/差分进化算法/多变异策略/高斯扰动/UCI数据库/聚类中心优化Key words
K-Means clustering algorithm/differential evolution algorithm/multiple mutation strategy/Gaussian perturbation/UCI database/cluster center optimization分类
信息技术与安全科学引用本文复制引用
刘红达,王福顺,孙小华,张广辉,王斌,何振学..一种基于改进差分进化的K-Means聚类算法研究[J].现代电子技术,2024,47(18):156-162,7.基金项目
河北省重点研发计划项目(22327403D) （22327403D）
