Operator Selection

Operator selection focuses on dynamically choosing the most effective algorithm or heuristic within a larger optimization process to improve efficiency and solution quality. Current research heavily utilizes reinforcement learning, often incorporating Q-learning or deep reinforcement learning architectures, to learn optimal operator selection policies from both offline and online experiences, sometimes leveraging graph neural networks to model problem structure. This adaptive approach enhances the performance of metaheuristics across various optimization problems, including those with constraints or multiple objectives, leading to improved solution quality and reduced computational costs in diverse applications.