论文标题
数据驱动的进化多目标优化基于偏离帕累托前后的多梯度下降
Data-Driven Evolutionary Multi-Objective Optimization Based on Multiple-Gradient Descent for Disconnected Pareto Fronts
论文作者
论文摘要
数据驱动的进化多目标优化（EMO）已被认为是具有昂贵目标功能的多目标优化问题的有效方法。当前的研究主要是针对“常规”三角形帕累托最佳前锋（PF）的问题开发的，而当PF由脱离连接的段组成时，性能可能会显着恶化。此外，当前数据驱动的emo中的后代再现并不能完全利用替代模型的潜在信息。考虑到这些考虑因素，本文提出了一种基于多级梯度下降的数据驱动的EMO算法。通过利用最新替代模型提供的规律性信息，它可以逐步探测一组具有收敛保证的分布式候选解决方案。此外，其填充标准建议一批有希望的候选解决方案进行昂贵的目标功能评估。 $ 33 $基准测试问题实例与断开PFS的实验完全证明了我们提出的方法对四种选定的同行算法的有效性。
Data-driven evolutionary multi-objective optimization (EMO) has been recognized as an effective approach for multi-objective optimization problems with expensive objective functions. The current research is mainly developed for problems with a 'regular' triangle-like Pareto-optimal front (PF), whereas the performance can significantly deteriorate when the PF consists of disconnected segments. Furthermore, the offspring reproduction in the current data-driven EMO does not fully leverage the latent information of the surrogate model. Bearing these considerations in mind, this paper proposes a data-driven EMO algorithm based on multiple-gradient descent. By leveraging the regularity information provided by the up-to-date surrogate model, it is able to progressively probe a set of well distributed candidate solutions with a convergence guarantee. In addition, its infill criterion recommends a batch of promising candidate solutions to conduct expensive objective function evaluations. Experiments on $33$ benchmark test problem instances with disconnected PFs fully demonstrate the effectiveness of our proposed method against four selected peer algorithms.
