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International Conference on Swarm Intelligence

ICSI 2016: Advances in Swarm Intelligence pp 500–511Cite as

Parameter Estimation of Vertical Two-Layer Soil Model via Brain Storm Optimization Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9712))

Abstract

A practical soil model is derived mathematically based on the measurement principles of Wenner’s method. The Wenner’s method is a conventional approach to measuring the apparent soil resistivity. This soil model constitutes two-soil layer with different properties vertically. Thus this model is called the vertical two-layer soil model. The motivation for the mathematical model is to estimate relevant parameters accurately from the data obtained from site measurements. This parameter estimation is in fact a challenging optimization problem. From the plotted graphs, this problem features a continuous but non-smooth landscape with a steep alley. This poses a great challenge to any optimization tool. Two prominent algorithms are applied, namely Gauss-Newton (GN) and Brain Storm Optimization (BSO). Results obtained conclude that the GN is fast but diverges due to bad starting points. On the contrary, the BSO is slow but it never diverges and is more stable.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under grant No. 61273367.

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Authors and Affiliations

  1. Xi’an Jiaotong-Liverpool University, Suzhou Industrial Park, Suzhou, Jiangsu, People’s Republic of China

    Tiew On Ting & Yuhui Shi

Authors
  1. Tiew On Ting
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  2. Yuhui Shi
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Correspondence to Tiew On Ting .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Ting, T.O., Shi, Y. (2016). Parameter Estimation of Vertical Two-Layer Soil Model via Brain Storm Optimization Algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_50

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  • DOI: https://doi.org/10.1007/978-3-319-41000-5_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40999-3

  • Online ISBN: 978-3-319-41000-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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