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

• URL: http://arxiv.org/abs/2604.00508v1
• Date: Wed, 01 Apr 2026 05:47:56 GMT
• Title: A Decoupled Basis-Vector-Driven Generative Framework for Dynamic Multi-Objective Optimization
• Abstract summary: This paper proposes a basis-vector-driven generative framework (DB-GEN) for dynamic multi-objective optimization.<n>It addresses non-linear coupling, negative transfer from outdated historical data, and the cold-start problem during environmental switches.<n> Experimental results demonstrate that DB-GEN improves tracking accuracy across various dynamic benchmarks compared to existing algorithms.
• Score: 13.136405879524856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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and data sparsity, primarily facing three challenges: the non-linear coupling of dynamic modes, negative transfer from outdated historical data, and the cold-start problem during environmental switches.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-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.

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