Metaheuristic algorithm selection system for continuous black-box optimization problems based on collaborative filtering

Selecting the best algorithm out of an algorithm set for a given problem is referred to as the algorithm selection (AS) problems. The importance of AS problems increases with the emerge of many optimization algorithms. Therefore, a five-star ranking system based on clustering is proposed, which maps the algorithm performance criteria to integers and reduces the ranking space. An algorithm set is prepared, including 24 commonly used optimization algorithms and four algorithms that win the CEC competition in 2016 and 2017. By testing the performance of the selected algorithms on 219 benchmark problems, a ranking matrix is obtained. The ranking matrix is used as the data source of the collaborative filtering (CF) algorithm to obtain a prediction model of algorithm ranking. For a new problem instance, the model predicts the ranking of all the algorithms in the algorithm set. The results show that the prediction accuracy is high, and over 90% of predicted best algorithms are capable of solving the problem instance. Sensitivity analysis shows that the proposed method can still maintain high prediction accuracy with limited prior information. © 2020, Editorial Office of Control and Decision. All right reserved.