Dynamic Optimization

Dynamic optimization focuses on solving optimization problems where the underlying problem parameters change over time, requiring algorithms that can adapt and track optimal solutions efficiently. Current research emphasizes developing robust and adaptable metaheuristics, including those incorporating reinforcement learning, evolutionary algorithms, and Bayesian optimization, often enhanced by techniques like dynamic mode decomposition or tensor networks for improved efficiency and generalization. These advancements are crucial for addressing real-world challenges across diverse fields, from resource management and control systems to machine learning and autonomous vehicle navigation, where static optimization methods fall short.