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<p>In this paper, a new enhanced grey wolf optimizer (<i>EGWO</i>) using a search space strategy based on the expectation value of prey probability distribution is introduced to magnify the range of the population and prevent premature convergence leading to accuracy improvement. Practically, it was applied to replicate the spatially distributed geo-layers in the Khersan dam site (southwest of Iran) using symmetry circular surrounded areas from sparse drilled borehole data, i.e., solve the high deployment costs and insufficient coverage in subsurface spatial distribution. The analytical comparison of <i>EGWO</i> with other presented algorithms was carried out using benchmark functions, nonparametric statical tests, and coverage optimization. Experimental results with a total of 88% coverage optimization and a range of 5.54 to 9.88% improvmenet rather than other proposed variants demonstrated the applicability of <i>EGWO</i> in solving the geospatial distribution coverage problems.</p>

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A new enhanced grey wolf optimizer to improve geospatially subsurface analyses

  • 期刊论文

  • 发表日期：

    2025年1月31日

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  • Reza Iraninezhad,
  • Reza Asheghi,
  • Hassan Ahmadi

摘要

In this paper, a new enhanced grey wolf optimizer (EGWO) using a search space strategy based on the expectation value of prey probability distribution is introduced to magnify the range of the population and prevent premature convergence leading to accuracy improvement. Practically, it was applied to replicate the spatially distributed geo-layers in the Khersan dam site (southwest of Iran) using symmetry circular surrounded areas from sparse drilled borehole data, i.e., solve the high deployment costs and insufficient coverage in subsurface spatial distribution. The analytical comparison of EGWO with other presented algorithms was carried out using benchmark functions, nonparametric statical tests, and coverage optimization. Experimental results with a total of 88% coverage optimization and a range of 5.54 to 9.88% improvmenet rather than other proposed variants demonstrated the applicability of EGWO in solving the geospatial distribution coverage problems.