基于平衡二叉搜索树的多目标A* 算法
Enhanced Multi-Objective A* Using Balanced Binary Search Trees
摘要：
这项工作解决了一个多目标最短路径问题（MO-SPP）的目标是找到一组帕累托最优的解决方案，从一个开始节点到图中的目的地。在文献中，已经开发了一系列基于MOA* 的方法来解决MO-SPP。通常，这些方法在搜索过程期间在每个节点处维持“边界”集合，以跟踪到达该节点的非支配部分路径。当目标的数量随着帕累托最优解的数量变大而增加时，这种搜索过程在计算上变得昂贵。在这项工作中，我们引入了一种新的方法，以有效地维护这些边界的多个目标，逐步构建平衡二叉搜索树内的MOA* 搜索框架。我们首先表明，我们的方法正确地找到了帕累托最优的前沿，然后提供了广泛的模拟结果的问题，三个，四个和五个目标，以表明我们的方法比现有的技术运行速度高达一个数量级。
英文摘要：
This work addresses a Multi-Objective Shortest Path Problem (MO-SPP) on a graph where the goal is to find a set of Pareto-optimal solutions from a start node to a destination in the graph. A family of approaches based on MOA* have been developed to solve MO-SPP in the literature. Typically, these approaches maintain a "frontier" set at each node during the search process to keep track of the non-dominated, partial paths to reach that node. This search process becomes computationally expensive when the number of objectives increases as the number of Pareto-optimal solutions becomes large. In this work, we introduce a new method to efficiently maintain these frontiers for multiple objectives by incrementally constructing balanced binary search trees within the MOA* search framework. We first show that our approach correctly finds the Pareto-optimal front, and then provide extensive simulation results for problems with three, four and five objectives to show that our method runs faster than existing techniques by up to an order of magnitude.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
