HV-Net: Hypervolume Approximation Based on DeepSets
提出HV-Net方法，利用具有排列不变性的深度神经网络DeepSets近似非支配解集的超体积，实验表明其近似误差和运行时间均优于传统点基和线基方法。
In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multiobjective 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 nondominated solution set. The input of HV-Net is a nondominated 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 techniques for hypervolume approximation.