Optimal design of IIR filters based on least p-norm using a novel meta-heuristic algorithm

In this work, an optimal infinite impulse response (IIR) filter is designed using least P-th norm and new meta-heuristic optimization algorithm called Crow Search Algorithm (CSA). The two approaches try to get a resolution to the specified problem but use unlike approaches to perform so. Furthermore, the p-norm is used to design optimum IIR filters. The main motivation of using CSA is its user-friendly optimization tool for both beginner and professional users. This Algorithm provides a good stability between variety and strength which are mainly controlled by the parameter of awareness probability. Some examples of IIR filter design cases are treated, and a comparison is done. It is revealed that the design objective is successfully attained, and the resultant filter parameters are better using the p-norm combined with the proposed CSA meta-heuristic optimization algorithms compared with IIR filter designed using butter synthesis approach in terms of accuracy and robustness. Classical approaches are largely outperformed by CSA for the same filter order.