Deep Reinforcement Learning for Smart Restarts in Exploration-Only Exploitation-Only Hybrid Metaheuristics

Metaheuristic hybrids equipped with multiple restarts have shown promise in complex optimization problems. A critical challenge in this domain, particularly for exploration-only exploitation-only hybrids, is determining optimal transition points between algorithms and restart locations. Each component of these hybrids excels in a specific task but may underperform in others, making transition and restart decisions crucial. This paper introduces an innovative solution to these challenges using reinforcement learning. We apply this approach to the UES-CMAES hybrid, training reinforcement learning agents to intelligently manage algorithm transitions and restarts. Evaluation on the CEC’13 benchmark suite demonstrates the efficacy of this method, indicating significant improvements in optimization performance. Our findings not only confirm the potential of reinforcement learning in enhancing metaheuristic hybrids but also pave the way for new research directions in intelligent optimization strategies.