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International Conference on Swarm, Evolutionary, and Memetic Computing

SEMCCO 2013: Swarm, Evolutionary, and Memetic Computing pp 625–638Cite as

Advanced Optimization by Progressive Mapping Search Method of PSO and Neural Network

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8298))

Abstract

This paper proposes a novel optimization approach by the fusion of the Progressive Mapping Search Method (PMSM) and the Neural Network (NN) aided Particle Swarm Optimization (PSO) that can obtain the global optimal solutions easily and speed up the overall search procedure. The PMSM merged with the NN and PSO has an important role as the navigation when the PSO is searching all the areas in order to acquire the optimum. It can help to improve the search capability of the original PSO method. That is, the PMSM together with the NN and PSO is trained to capture the PSO-searched solutions. To verify and demonstrate the effectiveness of our technique, we use a total of four test functions. The PMSM strategy employed in our paper is faster than the traditional PSO algorithm in all these four test functions. We also apply this new optimization scheme in the AVR (Automatic Voltage Regulator) system of the thermal power plant, which has resulted in faster and more stable responses.

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Author information

Authors and Affiliations

  1. Department of Electronic and Control of Engineering, Hanbat National University, S. Korea

    Dong Hwa Kim

  2. Research Center, Vision System Com, S. Korea

    Jin Ill Park

  3. Department of Automation and Systems Technology, Aalto University School of Electrical Engineering, Finland

    X. Z. Gao

Authors
  1. Dong Hwa Kim
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  2. Jin Ill Park
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  3. X. Z. Gao
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Editor information

Editors and Affiliations

  1. Electrical Engineering Dept, IIT Delhi, Hauz Khas, 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

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

    Swagatam Das

  4. SRM Engineering College, Department of Electrical and Electronics Engineering, SRM University, Potheri, Kattankulathur, 603203, Kancheepuram, Tamil Nadu, India

    Shubhransu Sekhar Dash

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© 2013 Springer International Publishing Switzerland

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Kim, D.H., Park, J.I., Gao, X.Z. (2013). Advanced Optimization by Progressive Mapping Search Method of PSO and Neural Network. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_56

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  • DOI: https://doi.org/10.1007/978-3-319-03756-1_56

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03755-4

  • Online ISBN: 978-3-319-03756-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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