A New Multi-Objective Optimization Method Based on Genetic- Fuzzy Algorithm and its Application in Induction Motor Speed Control

In this paper, a new method based on genetic-fuzzy algorithm for multi-objective optimization is proposed. This method is successfully applied to several multi-objective optimization problems. Two examples are presented: the first example is the optimization of two nonlinear mathematical functions and the second one is the design of PI controller for control of an induction motor drive supplied by Current-Source-Inverter (CSI). Step response of the system is considered and controller parameters are designed based on multi-objective optimization technique. Rise-time, maximum over-shoot, settling time and steady state error are considered as objective functions. The simulation results of the new method for induction motor speed control and optimization of two nonlinear mathematical functions are compared with the results obtained from other methods [4,14,15], which shows better performance.