ParetoTransport: Generative Optimization by Mass Transport Toward The Pareto Front

2026-09-07

AI Summary

本文提出ParetoTransport方法，通过质量传输将离线分布移向帕累托前沿，解决多目标优化中分布不均的问题。

Abstract

Offline multi-objective optimization requires not only moving the objective vectors of candidate designs toward the Pareto front, but also distributing them effectively along it. Generative methods have recently emerged as a natural approach because they learn a distribution over feasible designs while allowing generation to be steered toward promising designs. Existing methods, however, largely retain classical sample-wise guidance strategies, leaving the distribution-level modeling capability of generative methods underused. We propose ParetoTransport, a training-free guidance method for pre-trained flow-matching models that explicitly specifies and refines a population-level distribution in objective space. ParetoTransport guides a flow-matching sampler to iteratively transport the empirical offline distribution toward the Pareto front, with Wasserstein matching to intermediate proxy distributions. This directly controls distributional displacement and mass allocation along the front. We establish a convergence result and demonstrate state-of-the-art performance on standard offline MOO benchmarks, extending recent evaluations beyond hypervolume to generational distance, inverted generational distance, and Wasserstein distance.

Problem

• offline multi-objective optimization
• Pareto front
• generative methods
• distribution-level modeling

Innovation

• ParetoTransport
• flow-matching models
• Wasserstein matching
• distributional displacement
• mass allocation