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Automatic Shape Matching Using Improved Whale Optimization Algorithm with Atomic Potential Function

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Intelligent Computing Methodologies (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13395))

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Abstract

This paper an improved whale optimization algorithm (IWOA) with atomic potential matching (APM) for shape matching is proposed. The optimization process of shape matching is considered as a numerical optimization problem which can be conventionally exploited by the optimization algorithms. We improved whale optimization algorithm (WOA) where our modifications add the local pollination phase from the flower pollination algorithm. Then the IWOA based atomic potential matching (APM) model for searching the optimal shape matching. The comparative experiments with other algorithms for solving three different examples of shape matching problems demonstrate the feasibility and effectiveness. Meanwhile, the proposed shape matching algorithm proved to be superior to the comparative others swarm intelligence optimization algorithms, improved quality of results in all examples and significantly improve convergence speed.

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Acknowledgment

This work is supported by National Science Foundation of China under Grant No. 62066005, U21A20464.

Author information

Authors and Affiliations

  1. Xiangsihu College of Guangxi Minzu University, Nanning City, 530225, Guangxi, P.R. China

    Yuanfei Wei & Yongquan Zhou

  2. Faculty of Information Science and Technology, The National University of Malaysia, 43600 UKM, Bangi, Selangor, Malaysia

    Yuanfei Wei

  3. School of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, China

    Ying Ling, Qifang Luo & Yongquan Zhou

  4. Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Nanning, 530006, China

    Qifang Luo & Yongquan Zhou

Authors
  1. Yuanfei Wei
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  2. Ying Ling
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  3. Qifang Luo
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  4. Yongquan Zhou
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Corresponding author

Correspondence to Yuanfei Wei .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Wei, Y., Ling, Y., Luo, Q., Zhou, Y. (2022). Automatic Shape Matching Using Improved Whale Optimization Algorithm with Atomic Potential Function. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_71

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  • DOI: https://doi.org/10.1007/978-3-031-13832-4_71

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13831-7

  • Online ISBN: 978-3-031-13832-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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