An innovative approach of model order reduction using ant colony optimization

In this paper, a generic solution in the area of model order reduction is presented. The proposed method is based on artificial intelligence, which depends on a probabilistic optimization algorithm called Ant Colony. This approach aims to obtain a reduced-order model out of a relatively complex model represented as a transfer function. This reduced order model is obtained in a reasonable run time. It has acceptable error in time domain represented by the unit step time response, and the least error in frequency domain represented by Bode plot compared to other conventional methods in model order reduction regardless the targeted transfer function.