logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

A K-means-based cluster-driven optimization algorithm for solving global and engineering problems

delete2026-07-22
delete0
PRE
AI
H
Huhong Ren
Z
Zhanjiang Wang *
DOI：10.1016/j.asoc.2026.116014delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
• Adding the clustering idea similar to K-means to the algorithm iteration process. • Two sub-clusters use varied strategies, with the first adding centroid information. • Performing well on two test sets and 19 mechanical engineering optimization issues. • Combining simple structure with efficient global search and convergence ability. • The performance is also relatively stable on high-dimensional problems.

Journal

Applied Soft Computing cover
Applied Soft Computing
IF:
6.6
Papers:
1.4W
Citations:
4.8W

Organization

No organization information available