Embedding Q-Learning in the selection of metaheuristic operators: The enhanced binary grey wolf optimizer case

摘要

In the different situations present in the industry, combinatorial problems are increasingly frequent. This paper presents the interaction of Metaheuristics and Machine Learning, specifically as Machine Learning can be a support to enhance Metaheuristics. The resolution of the Set Covering Problem is presented, using the Grey Wolf Optimizer and Sine Cosine Algorithm metaheuristics that have been improved by adding a Q-Learning technique for the selection of a Discretization Scheme, using two-steps, intelligently choosing which transfer function to use and which binarization technique to apply in each iteration. The results show a better result for the Grey Wolf Optimizer with Q-Learning configuration, compared to other configurations in the literature, obtaining a better balance between exploration and exploitation.