Preference-Guided Diffusion for Multi-Objective Offline Optimization
This addresses the problem of generating diverse and high-quality solutions for offline multi-objective optimization, which is incremental as it builds on existing generative methods with a novel guidance mechanism.
The paper tackles offline multi-objective optimization by proposing a preference-guided diffusion model that generates Pareto-optimal designs, and it consistently outperforms other inverse/generative approaches while remaining competitive with forward/surrogate-based methods.
Offline multi-objective optimization aims to identify Pareto-optimal solutions given a dataset of designs and their objective values. In this work, we propose a preference-guided diffusion model that generates Pareto-optimal designs by leveraging a classifier-based guidance mechanism. Our guidance classifier is a preference model trained to predict the probability that one design dominates another, directing the diffusion model toward optimal regions of the design space. Crucially, this preference model generalizes beyond the training distribution, enabling the discovery of Pareto-optimal solutions outside the observed dataset. We introduce a novel diversity-aware preference guidance, augmenting Pareto dominance preference with diversity criteria. This ensures that generated solutions are optimal and well-distributed across the objective space, a capability absent in prior generative methods for offline multi-objective optimization. We evaluate our approach on various continuous offline multi-objective optimization tasks and find that it consistently outperforms other inverse/generative approaches while remaining competitive with forward/surrogate-based optimization methods. Our results highlight the effectiveness of classifier-guided diffusion models in generating diverse and high-quality solutions that approximate the Pareto front well.