Model Order Reduction of Higher Order Continuous Time Systems Using Intelligent Search Evolution Algorithm

In this paper a new model order reduction of linear continuous time system is proposed. An improved technique of Intelligent Search Evolution algorithm (ISEA) is used for generating the reduced order model. The proposed order reduction technique have the advantages of obtaining optimal model order reduction with less no of iterations and faster convergence with less computational time. The feasibility of proposed methodis explained with the help of Himmelblau function and model order reduction. The efficacy of the proposed order reduction technique is illustrated through a numerical example and results are compared with other existing model order reduction technique. The reduced order model generated by using ISEA will maintain the characteristics of original system very closely.