A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification

摘要: Fuzzy rule-based systems (FRBSs) are well-known soft computing methods commonly used to tackle classification problems characterized by uncertainties and imprecisions. We propose a hybrid intelligent fruit fly optimization algorithm (FOA) generate classify fuzzy rules select the best in if–then rule system. combine FOA heuristic algorithm. The is create, evaluate update triangular orthogonal systems. calculate certainty grade of rules. parameters proposed tuned using Taguchi method. An experiment with 27 benchmark datasets tenfold cross-validation strategy designed carried out compare nine different FRBSs. results show that this study significantly more accurate than competing