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<p>The Greylag Goose Optimizer (GGO) is an optimization method based on the social behavior and flight dynamics of greylag geese during migration process. Since its presentation in 2024, GGO has rapidly gained the attention of several researchers due to its capabilities for balancing the exploration-exploitation processes, its faster convergence capabilities, and its adaptability to be applied in several real-world optimization problems. As of October 2025, GGO has been the most cited metaheuristic method with over 700 citations. GGO has emerged as an influential optimization method, with 81.7% of its studies published in peer-reviewed journals and 18.3% presented at international conferences. This survey presents a depth study of GGO, covering from its foundational nature, algorithmic structure, improved variant strategies, and applications. Additionally, the performance of GGO has been study in the clustering-based image segmentation domain and compared to seven popular Metaheuristic Algorithms (MAs). Experimental study was conducted considering the evaluation of the Berkeley Segmentation Dataset (BSD), oral histopathology images, and White Blood Cell (WBC) images. The inherent algorithmic structure of GGO demonstrates its superior performance against state-of-the-art MAs in terms of fitness values, image quality, and statistical measurements.</p>

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Advances in Greylag Goose Optimizer: Principles, Improvements, and Performance Evaluation in Image Clustering Domain

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Totan Bharasa,
  • Arunita Das,
  • Krishna Gopal Dhal,
  • Kalyani Maity Das,
  • Jorge Gálvez

摘要

The Greylag Goose Optimizer (GGO) is an optimization method based on the social behavior and flight dynamics of greylag geese during migration process. Since its presentation in 2024, GGO has rapidly gained the attention of several researchers due to its capabilities for balancing the exploration-exploitation processes, its faster convergence capabilities, and its adaptability to be applied in several real-world optimization problems. As of October 2025, GGO has been the most cited metaheuristic method with over 700 citations. GGO has emerged as an influential optimization method, with 81.7% of its studies published in peer-reviewed journals and 18.3% presented at international conferences. This survey presents a depth study of GGO, covering from its foundational nature, algorithmic structure, improved variant strategies, and applications. Additionally, the performance of GGO has been study in the clustering-based image segmentation domain and compared to seven popular Metaheuristic Algorithms (MAs). Experimental study was conducted considering the evaluation of the Berkeley Segmentation Dataset (BSD), oral histopathology images, and White Blood Cell (WBC) images. The inherent algorithmic structure of GGO demonstrates its superior performance against state-of-the-art MAs in terms of fitness values, image quality, and statistical measurements.