Xiaoxia Jin

RLDMGO: a reinforcement learning-driven hybrid framework for complex optimization problems

This study suggests that RLDMGO can serve as a viable and adaptive solver for complex optimization problems and achieves a competitive ranking among fourteen evaluated state-of-the-art competitors.