Predetermined time constant approximation method for optimising search space boundary by standard genetic algorithm

Abstract

In this paper, a new predetermined time constant approximation (Tsp) method for optimising the search space boundaries to improve SGAs convergence is proposed. This method is demonstrated on parameter identification of higher order models. Using the dynamic response period and desired settling time of the transfer function coefficients offered a better suggestion for initial Tsp values. Furthermore, an extension on boundaries derived from the initial Tsp values and the consecutive execution, brought the elite groups within feasible boundary regions for better exploration. This enhanced the process of locating of the optimal values of coefficients for the transfer function. The Tsp method is investigated on two processes; excess oxygen and a third order continuous model with and without random disturbance. The simulation results assured the Tsp method's effectiveness and flexibility in assisting SGAs to locate optimal transfer function coefficients.