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Opposition Based Particle Swarm Optimizer with Ring Topology

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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

Particle Swarm Optimizer is population based global search algorithm mimicking the behavior of fish–schooling, bird’s flocking etc. Recently the opposition based learning scheme is incorporated in Particle Swarm Optimizer to improve its performance. Till now opposition based Particle Swarm Optimizer is implemented with gbest topology. This paper proposes the opposition based Particle Swarm Optimizer with lbest or ring topology. The proposed method is applied on 20 benchmark unconstrained functions. The obtained results are compared with other well–known opposition based Particle Swarm Optimizers with statistical analysis. The experimental results with statistical analysis show that the proposed algorithm outperforms over other algorithms for most of the functions.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bankura Unnayani Institute of Engineering, Bankura, West Bengal, India

    Tapas Si & Biplab Mandal

Authors
  1. Tapas Si
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  2. Biplab Mandal
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Corresponding author

Correspondence to Tapas Si .

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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Si, T., Mandal, B. (2015). Opposition Based Particle Swarm Optimizer with Ring Topology. 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_54

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  • DOI: https://doi.org/10.1007/978-3-319-20294-5_54

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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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