Reinforcement Learning Based Whale Optimizer

This work proposes a Reinforcement Learning based optimizer integrating SARSA and Whale Optimization Algorithm. SARSA determines the binarization operator required during the metaheuristic process. The hybrid instance is applied to solve benchmarks of the Set Covering Problem and it is compared with a Q-learning version, showing good results in terms of fitness, specifically, SARSA beats its Q-Learning version in 44 out of 45 instances evaluated. It is worth mentioning that the only instance where it does not win is a tie. Finally, thanks to graphs presented in our results analysis we can observe that not only does it obtain good results, it also obtains a correct exploration and exploitation balance as presented in the referenced literature.