A metaheuristic approach to two dimensional recursive digital filter design

Abhronil Sengupta, Tathagata Chakraborti, Amit Konar

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The two dimensional IIR digital filter design problem has received increased attention over the past few years. Recently, several metaheuristic algorithms have been employed in this domain and have produced promising results. Invasive Weed Optimization is one of the latest population-based metaheuristic algorithms that mimics the colonizing action of weeds. In this chapter, an improvement to the classical weed optimization algorithm has been proposed by introducing a constriction factor in the seed dispersal phase. Temporal Difference Q-Learning has been employed to adapt this parameter for different population members through the successive generations. Such hybridization falls under a special class of adaptive Memetic Algorithms. The proposed memetic realization, called Intelligent Invasive Weed Optimization (IIWO), has been applied to the two-dimensional recursive digital filter design problem and it has outperformed several competitive algorithms that have been applied in this research field in the past.

Original languageEnglish (US)
Title of host publicationAdvances in Heuristic Signal Processing and Applications
PublisherSpringer-Verlag Berlin Heidelberg
Pages167-182
Number of pages16
Volume9783642378805
ISBN (Electronic)9783642378805
ISBN (Print)364237879X, 9783642378799
DOIs
StatePublished - Jul 1 2013

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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  • Cite this

    Sengupta, A., Chakraborti, T., & Konar, A. (2013). A metaheuristic approach to two dimensional recursive digital filter design. In Advances in Heuristic Signal Processing and Applications (Vol. 9783642378805, pp. 167-182). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-37880-5_8