双编码动态培育遗传聚类算法及其在电池定制化配组中的应用  

Double-coded dynamic breeding based genetic clustering algorithm and its application in customized battery grouping

作　　者：王子赟 史伟杰 王艳 纪志成 WANG Zi-yun;SHI Wei-jie;WANG Yan;JI Zhi-cheng(Engineering Research Center of Internet of Things Technology Applications(Ministry of Education),Jiangnan University,Wuxi Jiangsu 214122,China)

机构地区：[1]江南大学物联网技术应用教育部工程研究中心,江苏无锡214122

出　　处：《控制理论与应用》2025年第1期118-126,共9页Control Theory & Applications

基　　金：国家重点研发计划项目(2020YFB1710600);江苏省自然科学基金面上项目(BK20221533);江苏省科协青年科技人才托举工程项目(TJ-2021-006)资助.

摘　　要：本文提出了一种基于双编码动态培育遗传聚类算法,同步编码聚类类别与聚类中心,引入动态培育思想,动态调整交叉对象与变异方向,进而增加微变异进而提高种群多样性,解决了传统遗传算法初期收敛缓慢和种群单一问题,提高了算法后期跳出局部最优的能力.同时,分析了双编码动态培育遗传聚类算法的收敛性.最后,以电池定制化配组为应用案例,验证了本文算法解决动力电池定制化配组问题的有效性,以及算法在收敛速度和全局搜索精度方面的优越性.A genetic clustering algorithm based on double coding dynamic cultivation is proposed,which encodes clustering categories and cluster centers at the same time,introduces the idea of dynamic cultivation,dynamically adjusts the cross object and mutation direction,increases micro-variation and thus increases population diversity.It solves the problem of slow convergence and single population in the initial stage of traditional genetic algorithm,and improves the ability of the algorithm to jump out of the local optimum in the later stage.At the same time,the convergence of the double-coded dynamic breeding genetic clustering algorithm is analyzed.Finally,this paper takes the battery customized combination as an application case and verifies that the algorithm is effective in solving the problem of customized matching of power battery,as well as its superiority in convergence speed and global search accuracy.

关 键 词：双编码 动态培育 遗传聚类 电池定制化配组 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]