论文标题
HV-NET：基于深度的超量近似
HV-Net: Hypervolume Approximation based on DeepSets
论文作者
论文摘要
在这封信中，我们提出了HV-NET，这是一种用于进化多目标优化的超量近似方法的新方法。 HV-NET的基本思想是使用DeepSet，即具有置换不变属性的深神经网络，以近似非主导溶液集的超量。 HV-NET的输入是目标空间中的一个非主导的解决方案集，输出是该解决方案集的近似超量值值。通过计算实验将HV-NET的性能与两种常用的Hypervolume近似方法（即基于点的方法和基于线路的方法）进行比较，从而评估了HV-NET的性能。我们的实验结果表明，HV-NET在近似误差和运行时都优于其他两种方法，这表明使用深度学习技术进行超vOLUME近似。
In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multi-objective optimization. The basic idea of HV-Net is to use DeepSets, a deep neural network with permutation invariant property, to approximate the hypervolume of a non-dominated solution set. The input of HV-Net is a non-dominated solution set in the objective space, and the output is an approximated hypervolume value of this solution set. The performance of HV-Net is evaluated through computational experiments by comparing it with two commonly-used hypervolume approximation methods (i.e., point-based method and line-based method). Our experimental results show that HV-Net outperforms the other two methods in terms of both the approximation error and the runtime, which shows the potential of using deep learning technique for hypervolume approximation.
