[2604.00508] A Decoupled Basis-Vector-Driven Generative Framework for Dynamic Multi-Objective Optimization

Computer Science > Machine Learning arXiv:2604.00508 (cs) [Submitted on 1 Apr 2026] Title:A Decoupled Basis-Vector-Driven Generative Framework for Dynamic Multi-Objective Optimization Authors:Yaoming Yang, Shuai Wang, Bingdong Li, Peng Yang, Ke Tang View a PDF of the paper titled A Decoupled Basis-Vector-Driven Generative Framework for Dynamic Multi-Objective Optimization, by Yaoming Yang and 4 other authors View PDF HTML (experimental) Abstract: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and data sparsity, primarily facing three challenges: the non-linear coupling of dynamic modes, negative transfer from outdated historical data, and the cold-start problem during environmental switches.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-GEN). First, to resolve non-linear coupling, the framework employs the discrete wavelet transform to separate evolutionary trajectories into low-frequency trends and high-frequency details. Second, to mitigate negative transfer, it learns transferable basis vectors via sparse dictionary learning rather than directly memorizing historical instances. Recomposing these bases under a topology-aware contrastive constraint constructs a structured latent manifold. Finally, to overcome the cold-start problem, a surrogate-assisted search paradigm samples initial populations from this manifold. Pre-trai...