Pareto Front Shape-Agnostic Pareto Set Learning in Multi-Objective Optimization
This addresses a key bottleneck for researchers and practitioners in optimization by enabling effective Pareto set learning without shape assumptions, though it is an incremental improvement over existing methods.
The paper tackles the challenge of Pareto set learning in multi-objective optimization, where existing methods require prior knowledge of the Pareto front shape for effective sampling, and proposes a shape-agnostic method that transforms arbitrary distributions into the Pareto set distribution, achieving high performance on diverse test problems compared to recent algorithms.
Pareto set learning (PSL) is an emerging approach for acquiring the complete Pareto set of a multi-objective optimization problem. Existing methods primarily rely on the mapping of preference vectors in the objective space to Pareto optimal solutions in the decision space. However, the sampling of preference vectors theoretically requires prior knowledge of the Pareto front shape to ensure high performance of the PSL methods. Designing a sampling strategy of preference vectors is difficult since the Pareto front shape cannot be known in advance. To make Pareto set learning work effectively in any Pareto front shape, we propose a Pareto front shape-agnostic Pareto Set Learning (GPSL) that does not require the prior information about the Pareto front. The fundamental concept behind GPSL is to treat the learning of the Pareto set as a distribution transformation problem. Specifically, GPSL can transform an arbitrary distribution into the Pareto set distribution. We demonstrate that training a neural network by maximizing hypervolume enables the process of distribution transformation. Our proposed method can handle any shape of the Pareto front and learn the Pareto set without requiring prior knowledge. Experimental results show the high performance of our proposed method on diverse test problems compared with recent Pareto set learning algorithms.