Space-Partitioned ND-Trees for the Dynamic Nondominance Problem
提出改进ND树效率的技术，用于动态非支配问题中构建和维护Pareto存档，通过空间分区或聚类方法重建树，数值实验表明间隔重建结合改进的边界更新策略可显著加速，尤其在高维情形。
We present techniques for improving the efficiency of ND-trees in the solution of the dynamic nondominance problem, i.e., for building and maintaining a Pareto archive. We propose algorithms for (re)building a tree from a set of nondominated points, either as a space-partitioned ND-tree or by a clustering approach. Numerical experiments confirm that rebuilding the tree at intervals, combined with modified strategies for updating the lower and upper bounds in each node and for computing the distances of points to subtrees in between the rebuilds, can lead to significant speedups over using plain ND-tree-based updates throughout, in particular for higher dimensions.