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Adaptive Balance Factor in Particle Swarm Optimization

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Proceedings of Sixth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 546))

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Abstract

Particle Swarm Optimization (PSO) is a refined optimization method, that has drawn interest of researchers in different areas because of its simplicity and efficiency. In standard PSO, particles roam over the search area with the help of two accelerating parameters. The proposed algorithm is tested over 12 benchmark test functions and compared with basic PSO and two other algorithms known as Gravitational search algorithm (GSA) and Biogeography based Optimization (BBO). The result reveals that ABF-PSO will be a competitive variant of PSO.

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Authors and Affiliations

  1. Rajasthan Technical University, Kota, India

    Siddhi Kumari Sharma & R. S. Sharma

Authors
  1. Siddhi Kumari Sharma
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  2. R. S. Sharma
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Correspondence to R. S. Sharma .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  2. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, National Institute of Technology, Silchar, Silchar, Assam, India

    Kedar Nath Das

  4. School of Mathematics, Thapar Institute of Engineering and Technology University, Patiala, Punjab, India

    Arvind Kumar Lal

  5. School of Mathematics, Thapar University Patiala, Patiala, Punjab, India

    Harish Garg

  6. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya K. Nagar

  7. Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

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Sharma, S.K., Sharma, R.S. (2017). Adaptive Balance Factor in Particle Swarm Optimization. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 546. Springer, Singapore. https://doi.org/10.1007/978-981-10-3322-3_2

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  • DOI: https://doi.org/10.1007/978-981-10-3322-3_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3321-6

  • Online ISBN: 978-981-10-3322-3

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