Prompt Learning for Generalized Vehicle Routing

• URL: http://arxiv.org/abs/2405.12262v1
• Date: Mon, 20 May 2024 15:42:23 GMT
• Title: Prompt Learning for Generalized Vehicle Routing
• Authors: Fei Liu, Xi Lin, Weiduo Liao, Zhenkun Wang, Qingfu Zhang, Xialiang Tong, Mingxuan Yuan,
• Abstract summary: This work investigates an efficient prompt learning approach in Neural optimization for cross-distribution adaptation. The proposed model learns a set of prompts among various distributions and then selects the best-matched one to prompt a pre-trained attention model for each problem instance. It also outperforms existing generalized models on both in-distribution prediction and zero-shot generalization to a diverse set of new tasks.
• Score: 17.424910810870273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural combinatorial optimization (NCO) is a promising learning-based approach to solving various vehicle routing problems without much manual algorithm design. However, the current NCO methods mainly focus on the in-distribution performance, while the real-world problem instances usually come from different distributions. A costly fine-tuning approach or generalized model retraining from scratch could be needed to tackle the out-of-distribution instances. Unlike the existing methods, this work investigates an efficient prompt learning approach in NCO for cross-distribution adaptation. To be concrete, we propose a novel prompt learning method to facilitate fast zero-shot adaptation of a pre-trained model to solve routing problem instances from different distributions. The proposed model learns a set of prompts among various distributions and then selects the best-matched one to prompt a pre-trained attention model for each problem instance. Extensive experiments show that the proposed prompt learning approach facilitates the fast adaptation of pre-trained routing models. It also outperforms existing generalized models on both in-distribution prediction and zero-shot generalization to a diverse set of new tasks. Our code implementation is available online https://github.com/FeiLiu36/PromptVRP.

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