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Particle Swarm Optimization Based on the Winner’s Strategy

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9873))

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Abstract

This paper presents particle swarm optimization based on winner’s strategy (PSO-WS). Instead of considering gbest and pbest particle for position update, each particle considers its distance from immediate winner to update its position. If this strategy performs well for the particle, then that particle updates its position based on this strategy, otherwise its position is replaced by its immediate winner particle’s position. Dimension dependant swarm size is used for better exploration. Proposed method is compared with CSO and CCPSO2, which are available to solve large scale optimization problems. Statistical results show that proposed method performs well for separable as well as non separable problems.

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Author information

Authors and Affiliations

  1. Department of CSE, RCOEM, Nagpur, India

    Shailendra S. Aote

  2. YCCE, Nagpur, India

    M. M. Raghuwanshi

  3. CSE, GHRCE, Nagpur, India

    L. G. Malik

Authors
  1. Shailendra S. Aote
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  2. M. M. Raghuwanshi
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  3. L. G. Malik
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Corresponding author

Correspondence to M. M. Raghuwanshi .

Editor information

Editors and Affiliations

  1. IIT, New Dehli, India

    Bijaya Ketan Panigrahi

  2. Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Indian Statistical Institute, Kolkata, India

    Swagatam Das

  4. Department of Computer Science Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India

    Suresh Chandra Satapathy

Appendix

Appendix

Seven benchmark functions are used in CEC-2008 test suit. These functions are summarized as follows.

Unimodal Functions (2):

  • F1: Shifted Sphere Function

  • F2: Shifted Schwefel’s

Multimodal Functions (5):

  • F3: Shifted Rosenbrock’s Function

  • F4: Shifted Rastrigin’s Function

  • F5: Shifted Griewank’s Function

  • F6: Shifted Ackley’s Function

  • F7: FastFractal “DoubleDip” Function

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Aote, S.S., Raghuwanshi, M.M., Malik, L.G. (2016). Particle Swarm Optimization Based on the Winner’s Strategy. In: Panigrahi, B., Suganthan, P., Das, S., Satapathy, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2015. Lecture Notes in Computer Science(), vol 9873. Springer, Cham. https://doi.org/10.1007/978-3-319-48959-9_18

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  • DOI: https://doi.org/10.1007/978-3-319-48959-9_18

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  • Online ISBN: 978-3-319-48959-9

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