A methodology for multi-label algorithm selection in constrained multiobjective optimization

Algorithm selection in optimization is often done by considering a single best-performing algorithm per problem. However, sometimes multiple algorithms perform comparably well on the same optimization problem, and in such cases, it would be appropriate to consider all of them as best performing. Hence, this work proposes an algorithm selection methodology that enables the identification and prediction of multiple algorithms as best performing. More specifically, the methodology involves first identifying the best-performing algorithms using statistical tests that show when the algorithms perform comparably well. Then, these algorithms are set as targets to machine learning models that can predict multiple algorithms as best performing. Finally, an evaluation measure is introduced to assess the performance of the algorithm selection models. The proposed methodology is applied to constrained multiobjective optimization. • A methodology is proposed that identifies multiple best-performing algorithms. • Identification of multiple best-performing algorithms relies on statistical tests. • Models for multi-label prediction of best-performing algorithms are assessed. • An evaluation metric for the proposed algorithm selection methodology is introduced. • The methodology is validated in constrained multiobjective optimization.