Multi-armed Bandit Based Hyper-Heuristics for the Permutation Flow Shop Problem

In this work, we propose MAB variants as selection mechanisms of a hyper-heuristic running on the multi-objective framework named MOEA/D-DRA to solve the Permutation Flow Shop Problem (PFSP). All the variants are designed to choose which of low-level heuristic components (for crossover and mutation operators) should be applied to each solution during execution. FRRMAB is the classical MAB, RMAB is restless and LinUCB is contextual (its context is based on side information). The proposed approaches are compared with each other and the best one, MOEA/D-LinUCB, is compared with MOEA/DDRA using the hypervolume indicator and nonparametric statistical tests. The results demonstrate the robustness of MAB-based approaches, especially the contextual-based one.