Local Search

Local search is a family of optimization algorithms that iteratively improve a solution by making small, localized changes. Current research focuses on enhancing efficiency and effectiveness through techniques like incorporating linkage learning to understand variable interactions, developing specialized operators for specific problem types (e.g., integer quadratic programming, stable matching), and integrating local search with other methods such as evolutionary algorithms, Bayesian optimization, and deep reinforcement learning. These advancements are improving the ability to solve complex problems across diverse fields, including operations research, machine learning, and robotics, by finding high-quality solutions more efficiently than previously possible.