Multi-objective based optimization for switched reluctance machines using fuzzy and genetic algorithms

Abstract

This paper presents a design methodology for sizing a preliminary design of a switched reluctance machine. The proposed method combines the use of genetic and fuzzy algorithms together to simplify the design method. Genetic algorithms (GA) are utilized for handling a multiple objective problem, whereas fuzzy algorithms (FA) simplify a definition of fitness evaluated functions for GA. Knowledge of design guidelines as well as specified dimensions is counted as the optimization objectives in the design process. Difficulty and complexity for describing an increased number of the fitness functions are declined by means of fuzzy description. Therefore, this method is much convenient to provide the means for multi-objective based optimization problems. An application is set to describe the functionalities of the proposed method. Simulation results verify that the improved GA with fuzzy algorithms gives better performances for the multi-objective optimization problems than those of conventional genetic algorithms.