UNCO: Towards Unifying Neural Combinatorial Optimization through Large Language Model

• URL: http://arxiv.org/abs/2408.12214v1
• Date: Thu, 22 Aug 2024 08:42:44 GMT
• Title: UNCO: Towards Unifying Neural Combinatorial Optimization through Large Language Model
• Authors: Xia Jiang, Yaoxin Wu, Yuan Wang, Yingqian Zhang,
• Abstract summary: We propose a unified neural optimization framework to solve different types of optimization problems (COPs) by a single model. We use natural language to formulate text-attributed instances for different COPs and encode them in the same embedding space by the large language model (LLM) Experiments show that the UNCO model can solve multiple COPs after a single-session training, and achieves satisfactory performance that is comparable to several traditional or learning-based baselines.
• Score: 21.232626415696267
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, applying neural networks to address combinatorial optimization problems (COPs) has attracted considerable research attention. The prevailing methods always train deep models independently on specific problems, lacking a unified framework for concurrently tackling various COPs. To this end, we propose a unified neural combinatorial optimization (UNCO) framework to solve different types of COPs by a single model. Specifically, we use natural language to formulate text-attributed instances for different COPs and encode them in the same embedding space by the large language model (LLM). The obtained embeddings are further advanced by an encoder-decoder model without any problem-specific modules, thereby facilitating a unified process of solution construction. We further adopt the conflict gradients erasing reinforcement learning (CGERL) algorithm to train the UNCO model, delivering better performance across different COPs than vanilla multi-objective learning. Experiments show that the UNCO model can solve multiple COPs after a single-session training, and achieves satisfactory performance that is comparable to several traditional or learning-based baselines. Instead of pursuing the best performance for each COP, we explore the synergy between tasks and few-shot generalization based on LLM to inspire future work.

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