Title
A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification
Document Type
Article
Publication Date
7-1-2017
DOI
https://doi.org/10.1007/s00521-017-3115-4
Abstract
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) to generate and classify fuzzy rules and select the best rules in a fuzzy if–then rule system. We combine a FOA and a heuristic algorithm in a hybrid intelligent algorithm. The FOA is used to create, evaluate and update triangular fuzzy rule-based and orthogonal fuzzy rule-based systems. The heuristic algorithm is used to calculate the certainty grade of the rules. The parameters in the proposed hybrid algorithm are tuned using the Taguchi method. An experiment with 27 benchmark datasets and a tenfold cross-validation strategy is designed and carried out to compare the proposed hybrid algorithm with nine different FRBSs. The results show that the hybrid algorithm proposed in this study is significantly more accurate than the nine competing FRBSs.
Language
English
Recommended Citation
Mousavi, Seyed Mohsen; Tavana, Madjid; Alikar, Najmeh; and Zandieh, Mostafa, "A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification" (2017). Business Systems and Analytics Faculty Work. 103.
https://digitalcommons.lasalle.edu/bsa_faculty/103
Comments
Mousavi, S.M., Tavana, M., Alikar, N. and Zandieh, M. (2019) ‘A Tuned Hybrid Intelligent Fruit Fly Optimization Algorithm for Fuzzy Rule Generation and Classification,’ Neural Computing and Applications, Vol. 31, No. 3, pp. 873-885.