Preference-Optimized Pareto Set Learning for Blackbox Optimization
This work addresses a computational bottleneck in multi-objective optimization for real-world experimental design, offering a more efficient method for exploring trade-offs, but it is incremental relative to existing Pareto set learning approaches.
The paper tackles the problem of approximating the entire Pareto set in multi-objective optimization by optimizing preference points for even distribution, which improves computational efficiency and accuracy in black-box scenarios. Results show efficacy on synthetic and real-world benchmarks, though no specific numerical gains are provided.
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.