Multi-objective Search via Lazy and Efficient Dominance Checks

Multi-objective search can be used to model many real-world problems that require finding Pareto-optimal paths from a specified start state to a speci-fied goal state, while considering different cost metrics such as distance, time, and fuel. The performance of multi-objective search can be improved by making dominance checking—an operation necessary to determine whether or not a path dominates another—more efficient. This was shown in practice by BOA* , a state-of-the-art bi-objective search algorithm, which outperforms previously existing bi-objective search algorithms in part because it adopts a lazy approach towards dominance checking. EMOA* , a recent multi-objective search algorithm, generalizes BOA* to more-than-two objectives using AVL trees for dominance checking. In this paper, we first propose Linear-Time Multi-Objective A* ( LTMOA* ), a multi-objective search algorithm that implements more efficient dominance checking than EMOA* using simple data structures like arrays. We then propose LazyLT-MOA* , which employs a lazier approach by removing dominance checking during node generation. Our experimental results show that LazyLTMOA* outperforms EMOA* by up to an order of magnitude in terms of runtime.

多目标搜索可用于建模许多现实世界中的问题，这些问题需要从指定的开始状态到指定目标状态，同时考虑了不同的成本指标，例如距离，时间和燃料的性能。 - 可以通过进行优势检查来改进目标搜索，这是确定路径是否主导的必要操作 - 在实践中显示了这一点。 BOA*是一种最先进的双目标搜索算法，该算法优于以前现有的双目标搜索算法，部分原因是它采用了懒惰的方法来检查EMOA*，这是一种最近的多目标搜索算法，它是一种懒惰的方法。将BOA*推广到本文中使用AVL树的比两个目标。多目标搜索算法使用简单的数据结构（如阵列）提出了更有效的优势检查*。 Emoa*在运行时最多达到数量级。