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An Improved PBIL Algorithm for Optimal Coalition Structure Generation of Smart Grids

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11154))

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Abstract

Coalition structure generation in multi-agent systems has long been a challenging problem because of its NP-hardness in computational complexity. In this paper, we propose a stochastic optimization approach that employs a modified population based incremental learning algorithm and a customized genotype encoding scheme to find the optimal coalition structure for smart grids with renewable energy sources. Empirical results show that the proposed approach gives competitive performance compared with existing solutions such as genetic algorithm and dynamic programming.

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Notes

  1. 1.

    Meteorological data obtained from “CliFlo: NIWA’s National Climate Database on the Web”, https://cliflo.niwa.co.nz/.

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Author information

Authors and Affiliations

  1. Department of Information Science, University of Otago, Dunedin, New Zealand

    Sean Hsin-Shyuan Lee, Jeremiah D. Deng, Martin K. Purvis & Maryam Purvis

  2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China

    Lizhi Peng

Authors
  1. Sean Hsin-Shyuan Lee
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  2. Jeremiah D. Deng
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  3. Martin K. Purvis
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  4. Maryam Purvis
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  5. Lizhi Peng
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Corresponding author

Correspondence to Jeremiah D. Deng .

Editor information

Editors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Mohadeseh Ganji

  2. University of Melbourne, Melbourne, VIC, Australia

    Lida Rashidi

  3. McGill University, Montreal, QC, Canada

    Benjamin C. M. Fung

  4. Griffith University, Gold Coast, QLD, Australia

    Can Wang

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Lee, S.HS., Deng, J.D., Purvis, M.K., Purvis, M., Peng, L. (2018). An Improved PBIL Algorithm for Optimal Coalition Structure Generation of Smart Grids. In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_33

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  • DOI: https://doi.org/10.1007/978-3-030-04503-6_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04502-9

  • Online ISBN: 978-3-030-04503-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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