# A Decoupled Basis-Vector-Driven Generative Framework for Dynamic Multi-Objective Optimization

arXiv: https://arxiv.org/abs/2604.00508 · HTML: https://mlsanity.com/paper/2604.00508/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 122 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 1 of 1)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)

## Abstract (arXiv)

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and data sparsity, primarily facing three challenges: the non-linear coupling of dynamic modes, negative transfer from outdated historical data, and the cold-start problem during environmental switches.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-trained on 120 million solutions, DB-GEN performs direct online inference without retraining or fine-tuning. This zero-shot generation process executes in milliseconds, requiring approximately 0.2 seconds per environmental change. Experimental results demonstrate that DB-GEN improves tracking accuracy across various dynamic benchmarks compared to existing algorithms.