On the performance of newsworthy meta-heuristic algorithms based on point of view fuzzy modelling

Abstract

Heuristic algorithms are used for optimizing a large diversity of engineering problems. In this study, the performance of six commonly used and cited meta-heuristic algorithms (MhAs) is compared based on point of view fuzzy modelling. In this study, the simplest version of MhAs is specifically used to observe and evaluate their natural optimization performances. For the comparisons, dynamic system modelling using a neuro-fuzzy system (NFS) was studied. Obtained results were evaluated based on commonly used statistical metrics, and then each algorithm was ranked according to these metrics. Based on the computed average ranks, the best MhA among the six MhAs was identified, and the effect of MhAs on fuzzy modelling was investigated.