The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. In the dynamic traveling salesman problem the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their Neighborhoods. The algorithm is tested with success on several large data sets.
G-LNS: Generative Large Neighborhood Search for LLM-Based Automatic Heuristic Design
Software College, Northeastern University（东北大学软件学院）；International Centre for Theoretical Physics Asia-Pacific, University of Chinese Academy of Sciences（中国科学院大学国际理论物理亚太中心）；Taiji Laboratory for Gravitational Wave Universe, University of Chinese Academy of Sciences（中国科学院大学太极引力波宇宙实验室）；Tsinghua University（清华大学）
Beyond Static Priors: Dynamic Neural Guidance for Large-Scale Ant Colony Optimization
Center for AI Research（人工智能研究中心）；VinUniversity（文大学）；College of Engineering and Computer Science（工程与计算机科学学院）；Laboratory for Information and Decision Systems（信息与决策系统实验室）；Massachusetts Institute of Technology（麻省理工学院）
A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem
Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）；School of Traffic and Transportation Engineering, Central South University（中南大学交通与运输工程学院）；Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology（埃因霍温理工大学工业工程与创新科学系）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动性学院）；School of Automation, Central South University（中南大学自动化学院）
