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<p>In this study, we present a novel approach to multi-threshold image segmentation using an adaptive method that combines the Ebola Optimization Search Algorithm (EOSA) with the Aquila Optimizer, termed the Integrated Enhanced Ebola Optimization Search Algorithm (IEOSA). Our approach leverages this integration to produce high-quality segmented images. The IEOSA method introduces two distinct optimization mechanisms to identify optimal solutions. By blending the randomness of the Aquila Optimizer with the capabilities of EOSA, we enhance the exploration potential of the algorithm. Additionally, we incorporate a self-transition learning system within the IEOSA to further boost its performance. To tackle multi-level threshold image segmentation, we apply Kapur’s entropy between-class variance within the IEOSA framework. Our findings show that the IEOSA-based techniques outperform other comparable methods, offering faster convergence and more stable segmentation results. Through comparative analysis using standard test images, we demonstrate that IEOSA achieves higher solution accuracy than other methods. Ultimately, the proposed IEOSA methodologies effectively address multi-level threshold image segmentation challenges, accurately segmenting even the minor errors that are often overlooked in high-resolution images.</p>

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A Control-based Transition Reinforced Optimization Process for Multi-level Threshold Image Segmentation

  • 期刊论文

  • 发表日期：

    2026年2月24日

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  • Wei Wang,
  • Peiying Zhang,
  • Saleh Ali Alomari,
  • Raed Abu Zitar,
  • Aseel Smerat,
  • Mohamed Sharaf,
  • Absalom E. Ezugwu,
  • Laith Abualigah

摘要

In this study, we present a novel approach to multi-threshold image segmentation using an adaptive method that combines the Ebola Optimization Search Algorithm (EOSA) with the Aquila Optimizer, termed the Integrated Enhanced Ebola Optimization Search Algorithm (IEOSA). Our approach leverages this integration to produce high-quality segmented images. The IEOSA method introduces two distinct optimization mechanisms to identify optimal solutions. By blending the randomness of the Aquila Optimizer with the capabilities of EOSA, we enhance the exploration potential of the algorithm. Additionally, we incorporate a self-transition learning system within the IEOSA to further boost its performance. To tackle multi-level threshold image segmentation, we apply Kapur’s entropy between-class variance within the IEOSA framework. Our findings show that the IEOSA-based techniques outperform other comparable methods, offering faster convergence and more stable segmentation results. Through comparative analysis using standard test images, we demonstrate that IEOSA achieves higher solution accuracy than other methods. Ultimately, the proposed IEOSA methodologies effectively address multi-level threshold image segmentation challenges, accurately segmenting even the minor errors that are often overlooked in high-resolution images.