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

Improved state transition algorithm with variable-capacity archive for constrained multiobjective optimization

delete2025-12-04
delete0
PRE
AI
J
Jie Han
C
Chen Xiao-long
D
Dan Su *
L
Liyang Qin
C
Chunhua Yang
DOI：10.1016/j.eswa.2025.130563delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
• A novel algorithm is proposed for CMOPs with narrow/disconnected feasible regions. • A variable-capacity archive dynamically balances objectives/constraints in evolution. • Improve three key state transition algorithm operators for fine-grained search. • 37 benchmarks and 10 real-world problems show it outperforms/rivals SOTA algorithms.

Journal

Expert Systems with Applications cover
Expert Systems with Applications
IF:
7.5
Papers:
2.9W
Citations:
10.2W

Organization

No organization information available