Design of two channel quadrature mirror filter bank: a multi-objective approach

Authors:
Subhrajit Roy;Sk. Minhazul Islam;Saurav Ghosh;Shizheng Zhao;Ponnuthurai Nagaratnam Suganthan;Swagatam Das
Affiliations:
Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Dept. of Electronics and Electrical Engg., Nanyang Technological Univrsity, Singapore;Dept. of Electronics and Electrical Engg., Nanyang Technological Univrsity, Singapore;Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India
Venue:
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
Year:
2011

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Abstract

In Digital Signal processing domain the Quadrature Mirror Filter (QMF) design problem is one of the most important problems of current interest. While designing a Quadrature Mirror Filter the goal of the designer is to achieve minimum values of Mean Square Error in Pass Band (MSEP), Mean Square Error in Stop Band (MSES), Square error of the overall transfer function of the QMF bank at the quadrature frequency and Measure of Ripple (mor). In contrast to the existing optimization-based methods that attempt to minimize a weighted sum of the four objectives considered here, in this article we consider these as four distinct objectives that are to be optimized simultaneously in a multi-objective framework. To the best of our knowledge, this is the first time to apply MO approaches to solve this problem. We use one of the best known Multi-Objective Evolutionary Algorithms (MOEAs) of current interest called NSGA-II as the optimizer. The multiobjective optimization (MO) approach provides greater flexibility in design by producing a set of equivalent final solutions from which the designer can choose any solution as per requirements. Extensive simulations reported shows that results of NSGA-II is superior to that obtained by two state-of-the-art single objective optimization algorithms namely DE and PSO.