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ISTOA: An Improved Sooty Tern Optimization Algorithm for Multilevel Threshold Image Segmentation

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Internet of Things, Smart Spaces, and Next Generation Networks and Systems (NEW2AN 2022)

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Abstract

This paper introduces an improved version of well-known Sooty Tern Optimization Algorithm (STOA). The improved version combines Opposition based learning (OBL) to introduce the Improved Sooty Tern Optimization Algorithm (ISTOA). The OBL strategy increases population diversity and avoids falling into local solutions. The efficiency of the proposed ISTOA is verified on multilevel threshold segmentation based on the objective functions of Kapur, and its performance is compared with the original algorithm and another metaheuristic algorithm. Experimental results reveal that the proposed ISTOA outperforms other algorithms in terms of fitness, peak signal-to-noise ratio, structural similarity, and segmentation findings.

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Author information

Authors and Affiliations

  1. Department of Information Systems, Mansoura University, Mansoura, Egypt

    Reham R. Mostafa

  2. Computer Science Department, University of Sharjah, Faculty of Science, Fayoum University, Sharjah, 27272, United Arab Emirates

    Ahmed M. Khedr

  3. Tashkent State University of Economics, Tashkent, Uzbekistan

    Ahmed Aziz

  4. Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmed Aziz

Authors
  1. Reham R. Mostafa
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  2. Ahmed M. Khedr
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  3. Ahmed Aziz
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Corresponding author

Correspondence to Ahmed Aziz .

Editor information

Editors and Affiliations

  1. Tampere University, Tampere, Finland

    Yevgeni Koucheryavy

  2. Tashkent State University of Economics, Tashkent, Uzbekistan

    Ahmed Aziz

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Mostafa, R.R., Khedr, A.M., Aziz, A. (2023). ISTOA: An Improved Sooty Tern Optimization Algorithm for Multilevel Threshold Image Segmentation. In: Koucheryavy, Y., Aziz, A. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN 2022. Lecture Notes in Computer Science, vol 13772. Springer, Cham. https://doi.org/10.1007/978-3-031-30258-9_12

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  • DOI: https://doi.org/10.1007/978-3-031-30258-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30257-2

  • Online ISBN: 978-3-031-30258-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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