Tabu Search
Tabu Search is a metaheuristic optimization algorithm used to solve complex combinatorial problems by exploring solution spaces beyond local optima. It employs memory structures, such as a tabu list, to prevent revisiting recently explored solutions and incorporates aspiration criteria to override tabu restrictions when beneficial. This approach helps avoid getting stuck in local minima and efficiently searches for high-quality solutions in large, complex domains.
Developers should learn Tabu Search when tackling NP-hard optimization problems like scheduling, routing, or resource allocation, where exhaustive search is infeasible. It is particularly useful in scenarios requiring near-optimal solutions within reasonable timeframes, such as logistics planning, telecommunications network design, or machine learning hyperparameter tuning. Its flexibility and ability to handle constraints make it a valuable tool in operations research and artificial intelligence applications.