Destroy Heuristic

Destroy heuristics are crucial components of metaheuristic optimization algorithms, aiming to intelligently disrupt existing solutions to escape local optima and find better ones. Current research focuses on improving the effectiveness of these heuristics, particularly within the context of large-scale problems like multi-agent pathfinding and the multidimensional knapsack problem, often employing techniques like adaptive selection, randomized repair, and neural network-based construction heuristics within frameworks such as Large Neighborhood Search (LNS) and Iterated Local Search (ILS). These advancements lead to significant improvements in solution quality and efficiency across various applications, including resource allocation, scheduling, and robotics.