AI RESEARCH
A Decoupled Basis-Vector-Driven Generative Framework for Dynamic Multi-Objective Optimization
arXiv CS.LG
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ArXi:2604.00508v1 Announce Type: new Dynamic multi-objective optimization requires continuous tracking of moving Pareto fronts. Existing methods struggle with irregular mutations and data sparsity, primarily facing three challenges: the non-linear coupling of dynamic modes, negative transfer from outdated historical data, and the cold-start problem during environmental switches. To address these issues, this paper proposes a decoupled basis-vector-driven generative framework (DB