POINTER

作品数：48被引量：19H指数：3
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相关机构：清华大学中国林业科学研究院河北省教育考试院石家庄市农林科学研究院更多>>
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Enhancing the generalization capability of 2D array pointer networks through multiple teacher-forcing knowledge distillation
《Journal of Automation and Intelligence》2025年第1期29-38,共10页Qidong Liu Xin Shen Chaoyue Liu Dong Chen Xin Zhou Mingliang Xu 
in part by the National Science Foundation of China under Grant No.62276238;in part by the National Science Foundation for Distinguished Young Scholars of China under Grant No.62325602;in part by the Natural Science Foundation of Henan,China under Grant No.232300421095.
The Heterogeneous Capacitated Vehicle Routing Problem(HCVRP),which involves efficiently routing vehicles with diverse capacities to fulfill various customer demands at minimal cost,poses an NP-hard challenge in combin...
关键词：Vehicle routing problem Multi-teacher knowledge distillation Teacher-forcing Pointer network 
Solving Combinatorial Optimization Problems with Deep Neural Network:A Survey
《Tsinghua Science and Technology》2024年第5期1266-1282,共17页Feng Wang Qi He Shicheng Li 
supported by the National Natural Science Foundation of China(Nos.62173258 and 61773296).
Combinatorial Optimization Problems(COPs)are a class of optimization problems that are commonly encountered in industrial production and everyday life.Over the last few decades,traditional algorithms,such as exact alg...
关键词：Combinatorial Optimization Problem(COPs) pointer network Transformer Graph Neural Network(GNN) Reinforcement Learning(RL) 
Learning to Branch in Combinatorial Optimization With Graph Pointer Networks
《IEEE/CAA Journal of Automatica Sinica》2024年第1期157-169,共13页Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao 
supported by the Open Project of Xiangjiang Laboratory (22XJ02003);Scientific Project of the National University of Defense Technology (NUDT)(ZK21-07, 23-ZZCX-JDZ-28);the National Science Fund for Outstanding Young Scholars (62122093);the National Natural Science Foundation of China (72071205)。
Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with c...
关键词：Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning 
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