Recently, Pareto Set Learning (PSL) has been proposed for learning the entire
Pareto set using a neural network. PSL employs preference vectors to scalarize
multiple objectives, facilitating the learning of mappings from preference
vectors to specific Pareto optimal solutions. Previous PSL methods have shown
their effectiveness in solving artificial multi-objective optimization problems
(MOPs) with uniform preference vector sampling. The quality of the learned
Pareto set is influenced by the sampling strategy of the preference vector, and
the sampling of the preference vector needs to be decided based on the Pareto
front shape. However, a fixed preference sampling strategy cannot
simultaneously adapt the Pareto front of multiple MOPs. To address this
limitation, this paper proposes an Evolutionary Preference Sampling (EPS)
strategy to efficiently sample preference vectors. Inspired by evolutionary
algorithms, we consider preference sampling as an evolutionary process to
generate preference vectors for neural network training. We integrate the EPS
strategy into five advanced PSL methods. Extensive experiments demonstrate that
our proposed method has a faster convergence speed than baseline algorithms on
7 testing problems. Our implementation is available at
this https URL

通过进化好性采样 (Evolutionary Preference Sampling，EPS) 策略，将其融入到五种高级 Pareto Set Learning (PSL) 方法中，该论文提出了一种有效的样本优先选择向量，实现了对 Pareto 集合的快速收敛速度。