Tabu Search

Tabu search is a metaheuristic optimization algorithm used to find good approximations to optimal solutions for computationally hard problems. Current research focuses on enhancing its performance through hybridization with other techniques, such as quantum-inspired algorithms, machine learning models (e.g., neural networks), and other optimization methods (e.g., differential evolution), and applying it to diverse domains. These advancements improve solution quality and efficiency across various applications, including scheduling problems (e.g., in healthcare and manufacturing), graph theory, and machine learning model optimization, leading to significant improvements in real-world problem-solving.