Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts. Recently, there has been a surge of interest in utilizing deep learning, especially deep reinforcement learning, to automatically learn effective solvers for CO. The resultant new paradigm is termed neural combinatorial optimization (NCO). However, the advantages and disadvantages of NCO relative to other approaches have not been empirically or theoretically well studied. This work presents a comprehensive comparative study of NCO solvers and alternative solvers. Specifically, taking the traveling salesman problem as the testbed problem, the performance of the solvers is assessed in five aspects, i.e., effectiveness, efficiency, stability, scalability, and generalization ability. Our results show that the solvers learned by NCO approaches, in general, still fall short of traditional solvers in nearly all these aspects. A potential benefit of NCO solvers would be their superior time and energy efficiency for small-size problem instances when sufficient training instances are available. Hopefully, this work would help with a better understanding of the strengths and weaknesses of NCO and provide a comprehensive evaluation protocol for further benchmarking NCO approaches in comparison to other approaches.

翻译：传统解决组合优化问题的求解器通常由人类专家设计。近年来，利用深度学习（尤其是深度强化学习）自动学习组合优化有效求解器的研究兴趣激增，由此产生的新范式被称为神经组合优化。然而，神经组合优化相较于其他方法的优势与不足尚未得到充分的实证或理论研究。本文对神经组合优化求解器与其他替代求解器进行了全面比较研究。具体而言，以旅行商问题为测试基准，从有效性、效率、稳定性、可扩展性和泛化能力五个维度评估求解器性能。结果表明，神经组合优化方法学习的求解器在几乎所有维度上仍普遍落后于传统求解器。神经组合优化求解器的潜在优势在于：当训练样本充足时，处理小规模问题实例具有更优的时间与能量效率。本研究有助于深入理解神经组合优化的优势与局限，并为后续将该方法与其他基准方法进行对比评估提供系统化评估框架。