[10] J. Bi, Y. Ma, J. Wang, Z. Cao, J. Chen, Y. Sun, and Y. M. Chee. Learning generalizable models for vehicle routing problems via knowledge distillation. arXiv preprint arXiv:2210.07686, 2022.
[11] C. Blum. Beam-aco—hybridizing ant colony optimization with beam search: An application to open shop scheduling. Computers & Operations Research, 32(6):1565–1591, 2005.
[12] A. Bogyrbayeva, M. Meraliyev, T. Mustakhov, and B. Dauletbayev. Learning to solve vehicle routing problems: A survey. arXiv preprint arXiv:2205.02453, 2022.
[13] X. Bresson and T. Laurent. The transformer network for the traveling salesman problem. arXiv preprint arXiv:2103.03012, 2021.
[14] E. K. Burke, M. Gendreau, M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and R. Qu. Hyperheuristics: A survey of the state of the art. Journal of the Operational Research Society, 64: 1695-1724, 2013.
[15] J. Cai, P. Wang, S. Sun, and H. Dong. A dynamic space reduction ant colony optimization for capacitated vehicle routing problem. Soft Computing, 26(17):8745-8756, 2022.
[16] J. Chen, Z. Zong, Y. Zhuang, H. Yan, D. Jin, and Y. Li. Reinforcement learning for practical express systems with mixed deliveries and pickups. ACM Transactions on Knowledge Discovery from Data (TKDD), 2022.
[17] X. Chen and Y. Tian. Learning to perform local rewriting for combinatorial optimization. Advances in Neural Information Processing Systems, 32, 2019.
[18] H. Cheng, H. Zheng, Y. Cong, W. Jiang, and S. Pu. Select and optimize: Learning to aolve large-scale tsp instances. In International Conference on Artificial Intelligence and Statistics, pages 1219–1231. PMLR, 2023.
[19] A. Chmiela, E. Khalil, A. Gleixner, A. Lodi, and S. Pokutta. Learning to schedule heuristics in branch and bound. Advances in Neural Information Processing Systems, 34:24235-24246, 2021.
[20] J. Choo, Y.-D. Kwon, J. Kim, J. Jae, A. Hottung, K. Tierney, and Y. Gwon. Simulation-guided beam search for neural combinatorial optimization. arXiv preprint arXiv:2207.06190, 2022.
[21] P. R. d O Costa, J. Rhuggenaath, Y. Zhang, and A. Akcay. Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning. In Asian Conference on Machine Learning, pages 465-480. PMLR, 2020.
[22] P. R. d O Costa, J. Rhuggenaath, Y. Zhang, and A. Akcay. Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning. In Asian conference on machine learning, pages 465-480. PMLR, 2020.
[23] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[24] M. Dorigo and T. Stützle. Ant colony optimization: overview and recent advances. Springer, 2019.
[25] M. Dorigo, V. Maniezzo, and A. Colorni. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1): 29-41, 1996.
[26] M. Dorigo, M. Birattari, and T. Stutzle. Ant colony optimization. IEEE computational intelligence magazine, 1(4):28-39, 2006.
[27] J. H. Drake, A. Kheiri, E. Ozcan, and E. K. Burke. Recent advances in selection hyper-heuristics. European Journal of Operational Research, 285(2):405-428, 2020.
[28] S. Elfwing, E. Uchibe, and K. Doya. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. (arXiv:1702.03118), Nov 2017. doi: 10.48550/arXiv.1702.03118. URL http://arxiv.org/abs/1702.03118.arXiv:1702.03118 [cs].