Coordinating Some Heuristics Using <i>Q</i>-learning for the Class of Single Objective Optimization Problems

Incorporating the features of genetic algorithm (GA) and particle swarm optimization (PSO), a new algorithm named PSO-GA is designed for single-objective continuous optimization problems (SOCOPs). Additional perturbation rules have been integrated into PSO to develop an enhanced heuristic named multi-rule PSO (MRPSO) for the same. Likewise, grey wolf optimizer (GWO) is modified by absorbing two more perturbation rules and is named multi-rule GWO (MRGWO). Also, GA and MRGWO are amalgamated to develop a new algorithm named GWO-GA. Finally, the merits of the two sets of heuristic approaches — {PSO, MRPSO, GA} and {GWO, EGWO, MRGWO} — are exploited using Q-learning to develop a hyper-heuristic, named PSO-GWO-Q, for the global optimization to SOCOPs, where the algorithm enhanced GWO (EGWO) is taken from the literature. All the algorithms have been tested against 50 benchmark test problems and it is observed that only PSO-GWO-Q provides results with a desired precision for the studied test problems. Comparing the consistency and efficiency of PSO-GWO-Q with some state-of-the-art algorithms for the SOCOPs using standard statistical tests, it is observed that the designed hyper-heuristic outperforms the others.