๐ค AI Summary
In offline multi-objective optimization (MOO), generative models struggle to effectively leverage design-label data and approximate high-quality Pareto fronts. Method: This paper proposes a flow-matching-based generative optimization framework that integrates three novel components: multi-objective weighted prediction guidance, local Pareto filtering, and neighborhood distribution evolution samplingโenabling uniform coverage of the weight space and cross-distribution knowledge transfer. Unlike single-objective guidance paradigms, our framework directly models the conditional generation flow over the Pareto front, enhancing both sampling directionality and diversity. Results: Evaluated on multiple benchmark tasks, the method significantly improves the coverage, convergence, and uniformity of the Pareto solution set. State-of-the-art performance demonstrates its effectiveness and generalizability across diverse MOO settings.
๐ Abstract
In offline multi-objective optimization (MOO), we leverage an offline dataset of designs and their associated labels to simultaneously minimize multiple objectives. This setting more closely mirrors complex real-world problems compared to single-objective optimization. Recent works mainly employ evolutionary algorithms and Bayesian optimization, with limited attention given to the generative modeling capabilities inherent in such data. In this study, we explore generative modeling in offline MOO through flow matching, noted for its effectiveness and efficiency. We introduce ParetoFlow, specifically designed to guide flow sampling to approximate the Pareto front. Traditional predictor (classifier) guidance is inadequate for this purpose because it models only a single objective. In response, we propose a multi-objective predictor guidance module that assigns each sample a weight vector, representing a weighted distribution across multiple objective predictions. A local filtering scheme is introduced to address non-convex Pareto fronts. These weights uniformly cover the entire objective space, effectively directing sample generation towards the Pareto front. Since distributions with similar weights tend to generate similar samples, we introduce a neighboring evolution module to foster knowledge sharing among neighboring distributions. This module generates offspring from these distributions, and selects the most promising one for the next iteration. Our method achieves state-of-the-art performance across various tasks.