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Design of Linear Phase FIR High Pass Filter Using PSO with Gaussian Mutation

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8947))

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Abstract

In this paper, a new optimization technique i.e. particle swarm optimization with Gaussian mutation (PSOGM) is used for the design of digital FIR High Pass filter and this technique is used to optimize filter coefficients. PSO with GM, the much improved version of particle swarm optimization algorithm (PSO), is a population based global search algorithm which finds near optimal solution in terms of a set of filter coefficients. Effectiveness of this algorithm is justified with a comparative study with real coded genetic algorithm (GA) and particle swarm optimization algorithm.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, ITER, SOA University, Bhubaneswar, India

    Archana Sarangi & Rasmita Lenka

  2. Department of Electronics and Instrumentation Engineering, ITER, SOA University, Bhubaneswar, India

    Shubhendu Kumar Sarangi

Authors
  1. Archana Sarangi
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  2. Rasmita Lenka
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  3. Shubhendu Kumar Sarangi
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Corresponding author

Correspondence to Shubhendu Kumar Sarangi .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

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© 2015 Springer International Publishing Switzerland

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Sarangi, A., Lenka, R., Sarangi, S.K. (2015). Design of Linear Phase FIR High Pass Filter Using PSO with Gaussian Mutation. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_41

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20293-8

  • Online ISBN: 978-3-319-20294-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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