Firefly artificial intelligence technique for model order reduction with substructure preservation

Abstract

Model order reduction (MOR) is a process of finding a lower order model for the original high order system with reasonable accuracy in order to simplify analysis, design, modeling and simulation for large complex systems. It is desirable that the reduced order model preserves the fundamental properties of the original system. This paper presents a new MOR technique of multi-input multi-output systems utilizing the firefly algorithm (FA) as an artificial intelligence technique. The reduction operation is proposed to maintain the exact dominant dynamics in the reduced order model with the advantage of substructure preservation. This is mainly possible for systems that are characterized as multi-time scale systems. Obtaining the reduced order model is achieved by minimizing the fitness function that is related to the error between the full and reduced order models’ responses. The new approach is compared with recently published work on firefly optimization for MOR, in addition to three other artificial intelligence techniques; namely, invasive weed optimization, particle swarm optimization and genetic algorithm. As a result, simulations show the potential of the FA for the process of MOR.