COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space
The article introduces COAgents, a multi-agent framework designed to address the complexities of Vehicle Routing Problems (VRP). This framework utilizes a cooperative approach to model the search process as a graph, enhancing adaptability across various tasks. Experimental results demonstrate that COAgents achieves competitive performance on benchmark instances, setting new records in learning-based methods for challenging VRPTW cases.
- ▪COAgents models the search process for routing problems as a graph with nodes representing solutions and edges for local refinements or large perturbations.
- ▪The framework includes a Partial Search Graph that dynamically constructs during the search to guide solution selection and exploration.
- ▪COAgents has shown to reduce the gap to the best-known solutions by significant margins on various benchmark instances.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20618 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2605.20618 (cs) [Submitted on 20 May 2026] Title:COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space Authors:Oleksandr Yakovenko, Mahdi Mostajabdaveh, Cheikh Ahmed, Abdullah Ali Sivas, Xiaorui Li, Zirui Zhou, Mao Kun View a PDF of the paper titled COAgents: Multi-Agent Framework to Learn and Navigate Routing Problems Search Space, by Oleksandr Yakovenko and 6 other authors View PDF HTML (experimental) Abstract:Although Vehicle Routing Problems (VRP) are essential to many real-world systems, they remain computationally intractable at scale due to their combinatorial complexity.
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