Traveling Salesman Problem

The Traveling Salesman Problem (TSP) seeks the shortest route visiting all points in a set and returning to the origin, a classic optimization challenge with broad applications. Current research focuses on enhancing existing heuristic solvers like Lin-Kernighan-Helsgaun (LKH) and Ant Colony Optimization (ACO) variants, as well as developing novel approaches using graph neural networks (GNNs), transformers, and reinforcement learning (RL) to improve solution quality and efficiency, particularly for large-scale and real-world instances with dynamic elements or constraints. These advancements are significant for various fields, including logistics, robotics, and infrastructure inspection, where efficient route planning is crucial for cost reduction and improved operational performance.