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Hybrid Particle Swarm Optimization Based on Thermodynamic Mechanism

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Simulated Evolution and Learning (SEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

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Abstract

This paper describes a thermodynamic particle swarm optimizer (TDPSO) based on the simple evolutionary equations. Inspired by the minimum free energy principle of the thermodynamic theoretics, a rating-based entropy (RE) and a component thermodynamic replacement (CTR) rule are implemented in the novel algorithm TDPSO. The concept of RE is utilized to systemically measure the fitness dispersal of the swarm with low computational cost. And the fitness range of all particles is divided into several ranks. Furthermore, the rule CTR is applied to control the optimal process with steeply fast convergence speed. It has the potential to maintain population diversity. Compared with the other improved PSO techniques, experimental results on some typical minimization problems show that the proposed technique outperforms other algorithms in terms of convergence speed and stability.

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Author information

Authors and Affiliations

  1. State Key Lab. of Software Engineering, Wuhan University, Wuhan, 430072, China

    Yu Wu, Yuanxiang Li, Xing Xu & Sheng Peng

Authors
  1. Yu Wu
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  2. Yuanxiang Li
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  3. Xing Xu
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  4. Sheng Peng
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Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

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© 2008 Springer-Verlag Berlin Heidelberg

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Wu, Y., Li, Y., Xu, X., Peng, S. (2008). Hybrid Particle Swarm Optimization Based on Thermodynamic Mechanism. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_29

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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