The Multi-objective Shortest Path (MOSP) problem is a classic network optimization problem that aims to find all Pareto-optimal paths between two points in a graph with multiple edge costs. Recent studies on multi-objective search with A* (MOA*) have demonstrated superior performance in solving difficult MOSP instances. This paper presents a novel search framework that allows efficient parallelization of MOA* with different objective orders. The framework incorporates a unique upper bounding strategy that helps the search reduce the problem's dimensionality to one in certain cases. Experimental results demonstrate that the proposed framework can enhance the performance of recent A*-based solutions, with the speed-up proportional to the problem dimension.
@article{arxiv.2503.10075,
title = {Parallelizing Multi-objective A* Search},
author = {Saman Ahmadi and Nathan R. Sturtevant and Andrea Raith and Daniel Harabor and Mahdi Jalili},
journal= {arXiv preprint arXiv:2503.10075},
year = {2025}
}