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On the Non Linear Dynamics of the Global Best Particle in Particle Swarm Optimization

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7677))

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Abstract

The dynamics of Particle swarm optimization has been developed from the collective behavior of the social creatures like fish schooling and gradually has become a powerful global optimization technique. In this paper we do the analysis on a continuous variant of PSO. The non linear dynamics of the global best particle is studied here and the exponential convergence is ensured. The effects of the different control parameters on the convergence of the global best particle are also studied.

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Author information

Authors and Affiliations

  1. Dept. of Electronics and Telecomm. Engineering, Jadavpur University, Kolkata, India

    Dipankar Maity & Udit Halder

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

    Swagatam Das

  3. Dept. of Electrical Engineering, Indian Institute of Technology, Delhi, India

    Bijaya Ketan Panigrahi

Authors
  1. Dipankar Maity
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  2. Udit Halder
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  3. Swagatam Das
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  4. Bijaya Ketan Panigrahi
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, 110016, Delhi, India

    Bijaya Ketan Panigrahi

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, 700108, Kolkata, India

    Swagatam Das

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Block N4, 2b-39, Nanyang Avenue, 639798, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electronics and Telecom. Engineering, Institute of Technical Education & Research, Siksha ’O’ Anusandhan University, 751030, Bhubaneswar, Odisha, India

    Pradipta Kumar Nanda

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© 2012 Springer-Verlag Berlin Heidelberg

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Maity, D., Halder, U., Das, S., Panigrahi, B.K. (2012). On the Non Linear Dynamics of the Global Best Particle in Particle Swarm Optimization. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_50

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  • DOI: https://doi.org/10.1007/978-3-642-35380-2_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35379-6

  • Online ISBN: 978-3-642-35380-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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