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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.10075 |
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| _version_ | 1866913733732728832 |
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| author | Ahmadi, Saman Sturtevant, Nathan R. Raith, Andrea Harabor, Daniel Jalili, Mahdi |
| author_facet | Ahmadi, Saman Sturtevant, Nathan R. Raith, Andrea Harabor, Daniel Jalili, Mahdi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10075 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parallelizing Multi-objective A* Search Ahmadi, Saman Sturtevant, Nathan R. Raith, Andrea Harabor, Daniel Jalili, Mahdi Artificial Intelligence 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. |
| title | Parallelizing Multi-objective A* Search |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.10075 |