Optimization of information granulation-oriented fuzzy set model using hierarchical fair competition-based parallel genetic algorithms

Abstract

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on information granulation and Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA and HCM method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods. © Springer-Verlag Berlin Heidelberg 2006.