ParetoPilot: Zero-Surrogate Offline Multi-Objective Optimization via Infer-Perturb-Guide Diffusion

Problem
This paper addresses the limitations of existing offline multi-objective optimization (MOO) methods that rely on surrogate models, which introduce computational overhead and can lead to deceptive evaluations. The authors highlight that while generative methods have shown promise, they typically require external surrogates that complicate the optimization process. This work is particularly relevant as it presents a preprint, indicating that it has not yet undergone peer review.

Method
The core contribution of this research is the ParetoPilot framework, which employs a zero-surrogate approach to offline MOO through a novel Infer-Perturb-Guide (IPG) engine. This engine operates within the unconditional denoising steps of a pre-trained diffusion model. The IPG engine consists of three main components:

1. Inference of Objective Direction: It infers the instantaneous objective direction by aligning conditional and unconditional noise predictions.
2. Perturbation Vector Generation: It constructs a perturbation vector using a mathematically orthogonalized gravity field for convergence and an edgeness-aware repulsive force to ensure diversity among solutions.
3. Guided Generation: The perturbation vector is integrated into the generation process using Classifier-Free Guidance (CFG), steering the model towards optimal solutions without the need for auxiliary proxy training.

The authors conducted extensive experiments across 51 tasks to validate the effectiveness of ParetoPilot.

Results
ParetoPilot demonstrated superior performance compared to 14 state-of-the-art surrogate-based and inverse generative baselines. The framework achieved significant hypervolume improvements, indicating better coverage of the Pareto front. Specific performance metrics were not disclosed in the abstract, but the results suggest a robust enhancement in both solution quality and diversity, outperforming existing methods in the offline MOO landscape.

Limitations
The authors acknowledge that while ParetoPilot eliminates the need for surrogate models, it may still be sensitive to the quality of the pre-trained diffusion models used. Additionally, the reliance on the inherent structure of the data may limit its applicability to datasets that do not conform to the assumptions made by the model. The paper does not address potential scalability issues when applied to larger datasets or more complex objective functions.

Why it matters
The implications of this work are significant for the field of offline MOO, as it provides a framework that enhances efficiency and performance without the drawbacks associated with surrogate models. By preserving data privacy and improving Pareto front coverage, ParetoPilot sets a new standard for future research in this area. This advancement could facilitate more effective design optimization in various engineering domains, as noted in the context of generative modeling and optimization strategies, as published in arXiv cs.NE.