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 https://github.com/rG223/EPS.

翻译：近期，帕累托集学习（PSL）被提出用于利用神经网络学习完整的帕累托集。PSL采用偏好向量对多个目标进行标量化处理，从而促进从偏好向量到特定帕累托最优解的映射学习。现有PSL方法在处理具有均匀偏好向量采样的人工多目标优化问题（MOPs）时已展现出有效性。学习得到的帕累托集质量受偏好向量采样策略影响，而偏好向量的采样需根据帕累托前沿形状进行决策。然而，固定的偏好采样策略无法同时适应多个MOPs的帕累托前沿。为解决这一局限，本文提出了一种进化偏好采样（EPS）策略，用于高效采样偏好向量。受进化算法启发，我们将偏好采样视为一个进化过程，以生成用于神经网络训练的偏好向量。我们将EPS策略集成到五种先进的PSL方法中。大量实验表明，在7个测试问题上，我们提出的方法比基线算法具有更快的收敛速度。我们的实现代码已开源：https://github.com/rG223/EPS。