Paper
CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem
Authors
Fengxiaoxiao Li, Xiao Mao, Mingfeng Fan, Yifeng Zhang, Yi Li, Tanishq Duhan, Guillaume Sartoretti
Abstract
Robotic systems often require a team of robots to collectively visit multiple targets while optimizing competing objectives, such as total travel cost and makespan. This setting can be formulated as the Multi-Objective Multiple Traveling Salesman Problem (MOMTSP). Although learning-based methods have shown strong performance on the single-agent TSP and multi-objective TSP variants, they rarely address the combined challenges of multi-agent coordination and multi-objective trade-offs, which introduce dual sources of complexity. To bridge this gap, we propose CAMO, a conditional neural solver for MOMTSP that generalizes across varying numbers of targets, agents, and preference vectors, and yields high-quality approximations to the Pareto front (PF). Specifically, CAMO consists of a conditional encoder to fuse preferences into instance representations, enabling explicit control over multi-objective trade-offs, and a collaborative decoder that coordinates all agents by alternating agent selection and node selection to construct multi-agent tours autoregressively. To further improve generalization, we train CAMO with a REINFORCE-based objective over a mixed distribution of problem sizes. Extensive experiments show that CAMO outperforms both neural and conventional heuristics, achieving a closer approximation of PFs. In addition, ablation results validate the contributions of CAMO's key components, and real-world tests on a mobile robot platform demonstrate its practical applicability.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.19074v1</id>\n <title>CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem</title>\n <updated>2026-03-19T15:59:45Z</updated>\n <link href='https://arxiv.org/abs/2603.19074v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.19074v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Robotic systems often require a team of robots to collectively visit multiple targets while optimizing competing objectives, such as total travel cost and makespan. This setting can be formulated as the Multi-Objective Multiple Traveling Salesman Problem (MOMTSP). Although learning-based methods have shown strong performance on the single-agent TSP and multi-objective TSP variants, they rarely address the combined challenges of multi-agent coordination and multi-objective trade-offs, which introduce dual sources of complexity. To bridge this gap, we propose CAMO, a conditional neural solver for MOMTSP that generalizes across varying numbers of targets, agents, and preference vectors, and yields high-quality approximations to the Pareto front (PF). Specifically, CAMO consists of a conditional encoder to fuse preferences into instance representations, enabling explicit control over multi-objective trade-offs, and a collaborative decoder that coordinates all agents by alternating agent selection and node selection to construct multi-agent tours autoregressively. To further improve generalization, we train CAMO with a REINFORCE-based objective over a mixed distribution of problem sizes. Extensive experiments show that CAMO outperforms both neural and conventional heuristics, achieving a closer approximation of PFs. In addition, ablation results validate the contributions of CAMO's key components, and real-world tests on a mobile robot platform demonstrate its practical applicability.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-03-19T15:59:45Z</published>\n <arxiv:comment>9 pages, 3 figures</arxiv:comment>\n <arxiv:primary_category term='cs.RO'/>\n <author>\n <name>Fengxiaoxiao Li</name>\n </author>\n <author>\n <name>Xiao Mao</name>\n </author>\n <author>\n <name>Mingfeng Fan</name>\n </author>\n <author>\n <name>Yifeng Zhang</name>\n </author>\n <author>\n <name>Yi Li</name>\n </author>\n <author>\n <name>Tanishq Duhan</name>\n </author>\n <author>\n <name>Guillaume Sartoretti</name>\n </author>\n </entry>"
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