The Multi-Objective Shortest-Path (MOS) problem finds a set of Pareto-optimal solutions from a start node to a destination node in a multi-attribute graph. The literature explores multi-objective A*-style algorithmic approaches to solving the NP-hard MOS problem. These approaches use consistent heuristics to compute an exact set of solutions for the goal node. A generalized MOS algorithm maintains a "frontier" of partial paths at each node and performs ordered processing to ensure that Pareto-optimal paths are generated to reach the goal node. The algorithm becomes computationally intractable at a higher number of objectives due to a rapid increase in the search space for non-dominated paths and the significant increase in Pareto-optimal solutions. While prior works have focused on algorithmic methods to reduce the complexity, we tackle this challenge by exploiting parallelism to accelerate the MOS problem. The key insight is that MOS algorithms rely on the ordered execution of partial paths to maintain high work efficiency. The proposed parallel algorithm (OPMOS) unlocks ordered parallelism and efficiently exploits the concurrent execution of multiple paths in MOS. Experimental evaluation using the NVIDIA GH200 Superchip's 72-core Arm-based CPU shows the performance scaling potential of OPMOS on work efficiency and parallelism using a real-world application to ship routing.

翻译：多目标最短路径（MOS）问题旨在多属性图中寻找从起始节点到目标节点的帕累托最优解集。现有文献主要探讨基于多目标A*风格的算法方法来解决这一NP难问题。这些方法利用一致启发式函数为目标节点计算精确解集。广义MOS算法在每个节点维护一个部分路径的“前沿”，并通过有序处理确保生成到达目标节点的帕累托最优路径。随着目标数量的增加，非支配路径的搜索空间急剧扩大，帕累托最优解数量显著增长，导致算法计算复杂度急剧上升。先前研究主要集中于通过算法方法降低复杂度，而本文则通过利用并行性来加速MOS问题的求解。关键洞见在于：MOS算法依赖部分路径的有序执行以维持较高的工作效率。本文提出的并行算法（OPMOS）实现了有序并行机制，有效利用了MOS中多条路径的并发执行。基于NVIDIA GH200超级芯片72核Arm架构CPU的实验评估，通过船舶航路规划的实际应用案例，证明了OPMOS在工作效率与并行性方面的性能扩展潜力。