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Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

Author

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  • Qingxin Liu

    (School of Computer Science and Technology, Hainan University, Haikou 570228, China)

  • Ni Li

    (School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
    Key Laboratory of Data Science and Intelligence Education of Ministry of Education, Hainan Normal University, Haikou 571158, China)

  • Heming Jia

    (School of Information Engineering, Sanming University, Sanming 365004, China)

  • Qi Qi

    (School of Computer Science and Technology, Hainan University, Haikou 570228, China)

  • Laith Abualigah

    (Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
    School of Computer Science, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia)

Abstract

Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio ( PSNR ), structure similarity ( SSIM ), and feature similarity ( FSIM ). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation.

Suggested Citation

  • Qingxin Liu & Ni Li & Heming Jia & Qi Qi & Laith Abualigah, 2022. "Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation," Mathematics, MDPI, vol. 10(7), pages 1-42, March.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1014-:d:776705
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    References listed on IDEAS

    as
    1. Ahmed A. Ewees & Laith Abualigah & Dalia Yousri & Ahmed T. Sahlol & Mohammed A. A. Al-qaness & Samah Alshathri & Mohamed Abd Elaziz, 2021. "Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation," Mathematics, MDPI, vol. 9(19), pages 1-25, September.
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    Citations

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    Cited by:

    1. Shuang Wang & Abdelazim G. Hussien & Heming Jia & Laith Abualigah & Rong Zheng, 2022. "Enhanced Remora Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(10), pages 1-32, May.
    2. Dejan G. Ćirić & Zoran H. Perić & Nikola J. Vučić & Miljan P. Miletić, 2023. "Analysis of Industrial Product Sound by Applying Image Similarity Measures," Mathematics, MDPI, vol. 11(3), pages 1-27, January.
    3. Honghua Rao & Heming Jia & Di Wu & Changsheng Wen & Shanglong Li & Qingxin Liu & Laith Abualigah, 2022. "A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(20), pages 1-36, October.
    4. Changsheng Wen & Heming Jia & Di Wu & Honghua Rao & Shanglong Li & Qingxin Liu & Laith Abualigah, 2022. "Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem," Mathematics, MDPI, vol. 10(19), pages 1-36, October.
    5. Di Wu & Honghua Rao & Changsheng Wen & Heming Jia & Qingxin Liu & Laith Abualigah, 2022. "Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 10(22), pages 1-41, November.
    6. Qingxin Liu & Ni Li & Heming Jia & Qi Qi & Laith Abualigah & Yuxiang Liu, 2022. "A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems," Mathematics, MDPI, vol. 10(9), pages 1-30, May.
    7. Alma Y. Alanis, 2022. "Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications," Mathematics, MDPI, vol. 10(13), pages 1-2, July.

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    2. Dejan G. Ćirić & Zoran H. Perić & Nikola J. Vučić & Miljan P. Miletić, 2023. "Analysis of Industrial Product Sound by Applying Image Similarity Measures," Mathematics, MDPI, vol. 11(3), pages 1-27, January.

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