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An Improved Ant Colony Optimization Based Particle Matching Algorithm for Time-Differential Pairing in Particle Tracking Velocimetry

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Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (ICIC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6216))

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Abstract

A new improved ant colony optimization (ACO) based algorithm has been developed for temporal particle matching in 2-D and 3-D particle tracking velocimetry (PTV). Two of the present authors have already applied the ant colony optimization (ACO) based algorithm effectively and successfully to the time differential particle pairing process of particle tracking velocimetry (PTV). In the present study, the algorithm has been further improved for the reduced com putation time as well as for the same or slightly better particle pairing results than that of the authors’ previous ACO algorithm. This improvement is mainly achieved due to the revision of the selection probability and pheromone update formulae devised specially for the purpose of accurate and fast computation. In addition, the new algorithm also provides better matching results when dealing with the loss-of-pair particles (i.e., those particles which exist in one frame but do not have their matching pair in the other frame), a typical problem in the real image particle tracking velocimetry. The performance of the new improved algorithm is tested with 2-D and 3-D standard particle images with successful results.

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Author information

Authors and Affiliations

  1. Dept. of Information Systems Engineering, Graduate Student of Faculty of Engineering, Osaka Sangyo University, Daito-shi, Osaka, 574-8530, Japan

    Sanjeeb Prasad Panday

  2. Dept. of Information Systems Engineering, Osaka Sangyo University, Daito-shi, Osaka, 574-8530, Japan

    Kazuo Ohmi & Kazuo Nose

Authors
  1. Sanjeeb Prasad Panday
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  2. Kazuo Ohmi
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  3. Kazuo Nose
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Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Intelligent Computing Laboratory, P.O. Box 1130, 230031, Hefei, Anhui, China

    De-Shuang Huang

  2. Department of Chemistry, University of Louisville, 2320 South Brook Street, 40292, Louisville, KY, USA

    Xiang Zhang

  3. Department of Computational Sciences, National Institute of Astrophysics Optics and Electronics, Luis E. Erro #1, 72840, Tonantzintla, Puebla, Mexico

    Carlos Alberto Reyes García

  4. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Lei Zhang

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© 2010 Springer-Verlag Berlin Heidelberg

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Panday, S.P., Ohmi, K., Nose, K. (2010). An Improved Ant Colony Optimization Based Particle Matching Algorithm for Time-Differential Pairing in Particle Tracking Velocimetry. In: Huang, DS., Zhang, X., Reyes García, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_43

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  • DOI: https://doi.org/10.1007/978-3-642-14932-0_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14931-3

  • Online ISBN: 978-3-642-14932-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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