Neighborhood Search

Neighborhood search is a family of optimization algorithms that iteratively improve solutions by exploring a defined "neighborhood" of similar solutions. Current research focuses on enhancing these algorithms through adaptive mechanisms, such as using machine learning to dynamically adjust neighborhood size and exploration strategies, and integrating them with other metaheuristics like variable neighborhood search and genetic algorithms. These advancements are improving the efficiency and solution quality for a wide range of complex problems, including multi-agent pathfinding, resource allocation, and vehicle routing, with significant implications for logistics, transportation, and other fields.