Huawei Technologies France SASU（华为技术法国公司）；Politecnico di Torino（都灵理工大学）
Matteo Boffa, Zied Ben Houidi, Jonatan Krolikowski, Dario Rossi
备注：
8 pages, 7 figures, accepted at AAAI'22 GCLR 2022 workshop on Graphs and more Complex structures for Learning and Reasoning
发表信息：
AAAI'22 GCLR 2022 workshop on Graphs and more Complex structures for Learning and Reasoning AAAI'22 GCLR 2022 workshop on Graphs and more Complex structures for Learning and Reasoning
Recent years have witnessed the promise that reinforcement learning, coupled with Graph Neural Network (GNN) architectures, could learn to solve hard combinatorial optimization problems: given raw input data and an evaluator to guide the process, the idea is to automatically learn a policy able to return feasible and high-quality outputs. Recent work have shown promising results but the latter were mainly evaluated on the travelling salesman problem (TSP) and similar abstract variants such as Split Delivery Vehicle Routing Problem (SDVRP). In this paper, we analyze how and whether recent neural architectures can be applied to graph problems of practical importance. We thus set out to systematically "transfer" these architectures to the Power and Channel Allocation Problem (PCAP), which has practical relevance for, e.g., radio resource allocation in wireless networks. Our experimental results suggest that existing architectures (i) are still incapable of capturing graph structural features and (ii) are not suitable for problems where the actions on the graph change the graph attributes. On a positive note, we show that augmenting the structural representation of problems with Distance Encoding is a promising step towards the still-ambitious goal of learning multi-purpose autonomous solvers.
Learning-Based TSP-Solvers Tend to Be Overly Greedy
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；School of Mathematics Sciences, University of Chinese Academy of Sciences（中国科学院大学数学科学学院）
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem
School of Computer Science, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Institute of Artificial Intelligence, Huazhong University of Science and Technology（华中科技大学人工智能研究院）；MIS, University of Picardie Jules Verne（皮卡第儒勒-凡尔纳大学MIS实验室）
Geometric Self-Supervised Pre-training for Neural Combinatorial Optimization
Universidad Politécnica de Madrid（马德里理工大学）；Information Processing and Telecomunications Center（信息处理与通信中心）；ETSI Telecomunicación（电信高等技术工程学院）；Grupo de Tratamiento de Imágenes（图像处理组）
Edge-wise Topological Divergence Gaps: Guiding Search in Combinatorial Optimization
Skoltech Moscow, Russia（斯克里普琴科莫斯科学院）；Skoltech（斯克里普琴科莫斯科学院）；AIRI Moscow, Russia（人工智能研究所莫斯科）；Skoltech, AI Foundation and Algorithm Lab Moscow, Russia（斯克里普琴科莫斯科学院）；Skoltech, CNRS Moscow, Russia（斯克里普琴科莫斯科学院）
Understanding Human-like Solutions in Combinatorial Optimization via Learning and Search
The University of Warwick（华威大学）；The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）；South China Normal University（华南师范大学）；The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone
Suleyman Demirel University（苏莱曼·德米雷尔大学）；UNIST（蔚山科学技术院）；Korea University（高丽大学）；Amazon（亚马逊）；University of South Florida（南佛罗里达大学）
强化学习最近在许多组合最优化问题的学习质量解决方案中显示出了希望。特别是，基于注意力的编码器-解码器模型在各种路由问题上表现出很高的效率，包括流动推销员问题（TSP）。不幸的是，它们在TSP with Drone（TSP-D）中表现不佳，需要协调不同的车辆-卡车和无人机。在TSP-D中，两辆车一前一后地移动，并且可能需要在节点处等待另一辆车加入。基于注意力的无状态解码器无法在车辆之间进行这种协调。我们提出了一个混合模型，它使用注意编码器和长短期记忆（LSTM）网络解码器，其中解码器的隐藏状态可以表示所做的动作序列。我们的经验表明，这样的混合模型提高了一个纯粹的基于注意力的模型的解决方案的质量和计算效率。最小-最大容量限制车辆路径问题（mmCVRP）的实验结果也表明，混合模型比基于注意力的模型更适合于多车辆的协调路径问题。所提出的模型展示了与运筹学基线方法相当的结果。
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Nanyang Technological University（南洋理工大学）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Computer Science, University of Science & Technology of China（中国科学技术大学计算机学院）；School of Mathematical and Computer Sciences, Heriot-Watt University（赫瑞瓦特大学数学与计算机科学学院）；College of Computer, National University of Defense Technology（国防科技大学计算机学院）
GELD: A Unified Neural Model for Efficiently Solving Traveling Salesman Problems Across Different Scales
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University（教育部符号计算与知识工程重点实验室、计算机科学与技术学院、吉林大学）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University（老年人积极生活卓越研究中 心（NTU-UBC合作）、南洋理工大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院、南洋理工大学）
A New Parallel Cooperative Landscape Smoothing Algorithm and Its Applications on TSP and UBQP
School of Mathematics and Statistics, Xi' an Jiaotong University, Xi' an, China（数学与统计学学院，西安交通大学，西安，中国）；Department of Computer Science, City University of Hong Kong, Hong Kong, China（计算机科学系，香港城市大学，香港，中国）
Nearest-Better Network for Visualizing and Analyzing Combinatorial Optimization Problems: A Unified Tool
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China（广东新型安全智能技术重点实验室，计算机科学与技术学院，哈尔滨工业大学，深圳 518055，中国）；School of Artificial Intelligence, Anhui University of Science and Technology, Hefei 231131, China（人工智能学院，安徽理工大学，淮北 231131，中国）；State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Huainan 232001, China（无人开采数字智能技术国家重点实验室，淮南 232001，中国）；School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, China（机械工程与电子信息学院，中国地质大学（武汉），中国）；School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, U.K.（计算机科学与信息学院，德蒙福特大学，莱斯特 LE1 9BH，英国）；Department of Computer Science, CINVESTAV‑IPN, Mexico City 07360, Mexico（计算机科学系，CINVESTAV‑IPN，墨西哥城 07360，墨西哥）；School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, N.L., Mexico（工程与科学学院，墨西哥蒙特雷技术学院，墨西哥）
LMask: Learn to Solve Constrained Routing Problems with Lazy Masking
Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；Department of Statistics and Data Science, University of Pennsylvania（宾夕法尼亚大学统计与数据科学系）
Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges
Queensland University of Technology（昆士兰科技大学）；Al-Ahliyya Amman University（安曼阿赫利亚大学）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Arab American University（阿拉伯美国大学）；Columbia University（哥伦比亚大学）；Minia University（米尼亚大学）
Baseline-Free Policy Optimization for Neural Combinatorial Optimization
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez（阿道夫·伊瓦涅斯大学工程与科学学院）；Dirección de Programas, Investigación y Desarrollo, Armada de Chile（智利海军计划、研究与发展局）；Millennium Nucleus for Social Data Science (SODAS)（千禧年社会数据科学核心（SODAS））；Millennium Nucleus in Data Science for Plant Resilience (PhytoLearning)（千禧年植物韧性数据科学核心（PhytoLearning））
Eyeballing Combinatorial Problems: A Case Study of Using Multimodal Large Language Models to Solve Traveling Salesman Problems
Queensland University of Technology（昆士兰科技大学）；Minia University（明亚大学）；Al-Ahliyya Amman University（安曼阿赫利亚大学）；Arab American University（阿拉伯美国大学）；Columbia University（哥伦比亚大学）
