A comparative study of information granulation-based fuzzy inference systems developed by means of space optimization algorithm (SOA) and genetic algorithms

In this study, we introduce a hybrid identification method of fuzzy inference systems based on the SOA and information granulation. The underlying idea of SOA comes from an analysis of the solution space. When dealing with the optimization of the fuzzy models, SOA leads not only to better search performance to find a global minimum but is also more computationally effective in comparison with the standard evolutionary algorithms (e.g. genetic algorithm). Information granulation realized with the aid of K-Means clustering is used to determine the initial values of the apex parameters of membership function of the fuzzy model. This method is used to carry out parameter estimation of the fuzzy models as well as to realize structure determination. The overall hybrid optimization of fuzzy inference systems comes in the form of two optimization mechanisms: a structural optimization and a parametric optimization. The structural optimization is supported by the SOA and K-Means while the parametric optimization is realized via SOA and a standard least squares method. The evaluation of the performance of the proposed model is presented by using a series of examples such as a Three-variable nonlinear function and Boston housing data. A comparative analysis demonstrates that SOA results in the improved performance both in terms of the quality of the model and the required computing time. Experimental results show that the proposed model leads to superior performance in comparison with the performance of some other fuzzy models reported in the literature.