強化学習の概念を導入したメタヒューリスティック法による組合せ最適化

抄録

Metaheuristics are promising methods for combinatorial optimization problems. However, existing metaheuristics cannot necessarily find optimal solutions or near-optimal solutions. Meanwhile, in the field of machine learning, reinforcement learning theoretically enables an agent to learn optimal actions and has attracted attention. Thus, we previously proposed a general metaheuristic framework based on the reinforcement learning. However, this framework is not superior in finding good solutions in a short time because it generates a relatively large number of infeasible solutions. In this paper, we propose an extension of the metaheuristic framework to find better solutions. In addition, we propose a concrete method based on the metaheuristic framework for solving the knapsack problem as a case study. The effectiveness of our proposed method is verified through experiments of applying the proposed method and other methods to the knapsack problem.