Large Neighborhood Search

Large Neighborhood Search (LNS) is a metaheuristic optimization technique that iteratively improves solutions by repeatedly destroying and reconstructing portions of a solution within a large search space. Current research emphasizes integrating LNS with machine learning, particularly reinforcement learning and neural networks, to enhance neighborhood selection and solution repair strategies, often applied to challenging problems like multi-agent pathfinding and vehicle routing. This approach demonstrates significant improvements in solution quality and efficiency across various applications, including robotics, logistics, and combinatorial optimization problems, impacting both theoretical understanding and practical deployment of optimization algorithms.