Choice of fitness functions and parameter settings in Genetic Algorithms for analysis of induction motors

Abstract

The paper proposes methods for enhancing the accuracy and speed of Genetic Algorithms when determining the parameters of induction motors. A systematic study of the impact of genetic operators and algorithm parameters on optimization process performance is done. New fitness functions are developed containing additional quantities such as the input power of motors. Various genetic operator types and constraints are analysed providing an insight into a specific settings choice. Special attention is given to the factors generating stochastic noise and the ways to eliminate it. To evaluate the effectiveness of the proposed fitness functions and parameter settings, ten combinatorial optimization experiments are conducted on two types of induction motors. The results show that adequate fitness functions, combined with proper genetic operators setups, can significantly enhance the accuracy and speed of Genetic Algorithms while reducing the required input data.