抽象的

Opposition aided Cat Swarm Optimization Algorithm for Optimal Digital IIR High Pass Filter Design

Kamalpreet Kaur Dhaliwal, Jaspreet Singh Dhillon

This paper presents a solution methodology for the designing of optimal and stable digital infinite impulse response (IIR) high pass (HP) filter by employing the cat swarm optimization (CSO) technique with oppositional learning. Because of the presence of the denominator terms, the error surface of digital IIR filters is non linear and multimodal. Therefore, the traditional designing techniques usually got trapped in the local minim. CSO is a population based global optimization technique which has global as well as local search capabilities. Here, the multicriterion optimization is used as the design criterion that undertakes the minimization of magnitude approximation error and minimization of ripple magnitudes while satisfying the stability constraints that are imposed during the design process. For the intent of starting with an improved solution set, the opposition based learning strategy is included in CSO. The developed algorithm is used to design the digital IIR high pass (HP) filter and attempts to find the optimal filter coefficients which are approximately close to the desired filter response. The computational results shows that the proposed algorithm is capable of designing the stable and optimal digital IIR HP filter structure that is better to the designs presented by other algorithms.