Abstract
The analytical study of large-scale linear time-invariant systems is a very tedious and complicated task in a category of real-life optimization problems. So, simplification procedures for these complex problems are needed. In the solution tactic of this complex problem, Model Order Reduction (MOR) is a novel concept providing a simpler model than the original one based on mathematical approximation. In literature, several meta-heuristics are employed to solve MOR problem. In the same line of order, this chapter presents a technique to solve MOR problem using modified BAT algorithm based on levy flight and opposition based learning. The concept of Levy flight random walk and opposition based learning (OBL) is embedded to BAT algorithm (BA) to avoid local optima trapping and to enhance the exploitation and exploration ability. To evaluate the performance of the proposed methodology, it is tested over three different MOR problems of different transfer functions. The numerical and statistical results confirmed the supremacy of the proposed variant in terms of stability of reduced order systems.
Published Version
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