Preference-Optimized Pareto Set Learning for Blackbox Optimization

TL;DR

This paper introduces a preference-optimized Pareto set learning method for multi-objective black-box optimization, improving the approximation of the Pareto front by optimizing preference points for better exploration.

Contribution

It proposes a bilevel optimization approach to adaptively distribute preference points on the Pareto front, enhancing Pareto set learning efficiency and accuracy.

Findings

Effective on synthetic benchmark problems

Demonstrated improved Pareto front approximation

Applicable to real-world black-box optimization tasks

Abstract

Multi-Objective Optimization (MOO) is an important problem in real-world applications. However, for a non-trivial problem, no single solution exists that can optimize all the objectives simultaneously. In a typical MOO problem, the goal is to find a set of optimum solutions (Pareto set) that trades off the preferences among objectives. Scalarization in MOO is a well-established method for finding a finite set approximation of the whole Pareto set (PS). However, in real-world experimental design scenarios, it's beneficial to obtain the whole PS for flexible exploration of the design space. Recently Pareto set learning (PSL) has been introduced to approximate the whole PS. PSL involves creating a manifold representing the Pareto front of a multi-objective optimization problem. A naive approach includes finding discrete points on the Pareto front through randomly generated preference vectors and connecting them by regression. However, this approach is computationally expensive and leads to a poor PS approximation. We propose to optimize the preference points to be distributed evenly on the Pareto front. Our formulation leads to a bilevel optimization problem that can be solved by e.g. differentiable cross-entropy methods. We demonstrated the efficacy of our method for complex and difficult black-box MOO problems using both synthetic and real-world benchmark data.