Intelligent information technology for structural optimization of fuzzy control and decision-making systems

Abstract

This chapter is devoted to the development and research of intelligent information technology (IIT) for structural optimization of fuzzy systems (FS) based on the evolutionary search of the optimal membership functions. The proposed IIT uses a combination of different (two or more) bioinspired evolutionary algorithms and allows finding the optimal membership functions of linguistic terms at solving the compromise problems of multi-criteria structural optimization of various FSs to increase their efficiency, as well as to reduce the degree of complexity of further parametric optimization. To study the effectiveness of the considered IIT, the search of the optimal membership functions is conducted for a FS of the multi-purpose mobile robot (MR) designed to move along inclined and vertical ferromagnetic surfaces, with the implementation of three evolutionary algorithms: genetic, artificial immune systems, biogeographic. The analysis of the obtained results showed that the usage of the proposed IIT gives the opportunity to significantly increase the efficiency of the MR control, as well as to reduce the total number of parameters at further parametric optimization of linguistic terms, which confirms its high efficiency.