Feedback-driven adaptive variable grouping for decomposition-based large-scale multiobjective optimization

Qi Liu

doi:10.1016/j.swevo.2026.102416

Feedback-driven adaptive variable grouping for decomposition-based large-scale multiobjective optimization

2026-05-30

0

PRE

AI

DOI：10.1016/j.swevo.2026.102416

Original

Original request for help

Save

Abstract

En 中文

• Lightweight variable classification reduces cost from O(D⋅N) to O(D). • Adaptive regrouping dynamically refines variable groups during evolution. • Group-level operator selection balances exploration and exploitation. • MOEA/D-FAVG outperforms seven LSMaOEAs on LSMOP and DTLZ tests. • Theoretical analysis proves error bounds and convergence properties.

Feedback-driven adaptive variable grouping for decomposition-based large-scale multiobjective optimization

Abstract

Journal

Organization