CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem

• URL: http://arxiv.org/abs/2603.19074v1
• Date: Thu, 19 Mar 2026 15:59:45 GMT
• Title: CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem
• Abstract summary: We present CAMO, a conditional neural solver for the Multi-Objective Multiple Traveling Salesman Problem (MOMTSP)<n>It generalizes across varying numbers of targets, agents, and preference vectors, and yields high-quality approximations to the Pareto front (PF)<n>Experiments show that CAMO outperforms both neural and conventionals, achieving a closer approximation of PFs.
• Score: 13.232337043603161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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