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Empirical Assessment of Human Learning Principles Inspired PSO Algorithms on Continuous Black-Box Optimization Testbed

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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 benchmarks the performance of one of the recent research directions in the performance improvement of particle swarm optimization algorithm; human learning principles inspired PSO variants. This article discusses and provides performance comparison of nine different PSO variants. The Comparing Continuous Optimizers (COCO) methodology has been adopted in comparing these variants on the noiseless BBOB testbed, providing useful insight regarding their relative efficiency and effectiveness. This study provides the research community a comprehensive account of suitability of a PSO variant in solving selective class of problems under different budget settings. Further, certain rectifications/extensions have also been suggested for the selected PSO variants for possible performance enhancement. Overall, it has been observed that SL-PSO and MePSO are most suited for expensive and moderate budget settings respectively. Further, iSRPSO and TPLPSO have provided better solutions under cheap budget settings where iSRPSO has shown robust behaviour (better solutions over dimensions). We hope this paper would mark a milestone in assessing the human learning principles inspired PSO algorithms and used as a baseline for performance comparison.

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Acknowledgement

The authors wish to extend their thanks to the ATMRI:2014-R8, Singapore, for providing financial support to conduct this study.

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Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    M. R. Tanweer, Abdullah Al-Dujaili & S. Suresh

Authors
  1. M. R. Tanweer
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  2. Abdullah Al-Dujaili
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  3. S. Suresh
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Corresponding author

Correspondence to M. R. Tanweer .

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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

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Tanweer, M.R., Al-Dujaili, A., Suresh, S. (2016). Empirical Assessment of Human Learning Principles Inspired PSO Algorithms on Continuous Black-Box Optimization Testbed. 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_2

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  • DOI: https://doi.org/10.1007/978-3-319-48959-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48958-2

  • Online ISBN: 978-3-319-48959-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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