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<p>The Grey Wolf Optimization (GWO) algorithm is acknowledged as an effective method for rock acoustic emission localization. However, the conventional GWO algorithm encounters challenges related to solution accuracy and convergence speed. To address these concerns, this paper develops a Simplex Improved Grey Wolf Optimizer (SMIGWO) algorithm. The randomly generating initial populations are replaced with the iterative chaotic sequences. The search process is optimized using the convergence factor optimization algorithm based on the inverse incomplete <i>Г</i> function. The simplex method is utilized to address issues related to poorly positioned grey wolves. Experimental results demonstrate that, compared to the conventional GWO algorithm-based AE localization algorithm, the proposed algorithm achieves a higher solution accuracy and showcases a shorter search time. Additionally, the algorithm demonstrates fewer convergence steps, indicating superior convergence efficiency. These findings highlight that the proposed SMIGWO algorithm offers enhanced solution accuracy, stability, and optimization performance. The benefits of the SMIGWO algorithm extend universally across various materials, such as aluminum, granite, and sandstone, showcasing consistent effectiveness irrespective of material type. Consequently, this algorithm emerges as a highly effective tool for identifying acoustic emission signals and improving the precision of rock acoustic emission localization.</p>

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Localization of Acoustic Emission Source in Rock Using SMIGWO Algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月26日

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  • Jiong Wei,
  • Fuqiang Gao,
  • Jinfu Lou,
  • Lei Yang,
  • Xiaoqing Wang

摘要

The Grey Wolf Optimization (GWO) algorithm is acknowledged as an effective method for rock acoustic emission localization. However, the conventional GWO algorithm encounters challenges related to solution accuracy and convergence speed. To address these concerns, this paper develops a Simplex Improved Grey Wolf Optimizer (SMIGWO) algorithm. The randomly generating initial populations are replaced with the iterative chaotic sequences. The search process is optimized using the convergence factor optimization algorithm based on the inverse incomplete Г function. The simplex method is utilized to address issues related to poorly positioned grey wolves. Experimental results demonstrate that, compared to the conventional GWO algorithm-based AE localization algorithm, the proposed algorithm achieves a higher solution accuracy and showcases a shorter search time. Additionally, the algorithm demonstrates fewer convergence steps, indicating superior convergence efficiency. These findings highlight that the proposed SMIGWO algorithm offers enhanced solution accuracy, stability, and optimization performance. The benefits of the SMIGWO algorithm extend universally across various materials, such as aluminum, granite, and sandstone, showcasing consistent effectiveness irrespective of material type. Consequently, this algorithm emerges as a highly effective tool for identifying acoustic emission signals and improving the precision of rock acoustic emission localization.