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<p>This paper discusses the recent developments of the Grey Wolf Optimization algorithm, its improvements, practical applications, challenges, and future potential. WSNs, which are based on the accurate localization of nodes, often face noisy distance measurements and uneven distribution of anchor nodes. These factors reduce localization accuracy and increase the computational demands for some nodes. In this paper, an attempt is made to solve these issues by developing a GWO-based localization method for WSNs. In this proposed method, weights are assigned to anchor nodes to minimize the effects of measurement errors and enhance accuracy. Optimizing node selection is also achieved to counterbalance uneven anchor distribution and adapt to changes in network conditions. Thus, with refined distance estimations between nodes, the GWO algorithm achieves better performance compared to traditional anchor selection techniques. Experimental results show that the method proposed here drastically improves localization precision, and hence a promising solution can be used to improvise WSN performance. Enhanced GWO algorithms are highlighted with the potential in creating more reliable and efficient processes for node localization.</p>

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Enhancing Grey Wolf Optimization with Applications and Innovations

  • 期刊论文

  • 发表日期：

    2025年4月7日

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  • Durga Praveen Deevi,
  • Sharadha Kodadi,
  • Himabindu Chetlapalli,
  • Naga Sushma Allur,
  • Koteswararao Dondapati,
  • Thinagaran Perumal

摘要

This paper discusses the recent developments of the Grey Wolf Optimization algorithm, its improvements, practical applications, challenges, and future potential. WSNs, which are based on the accurate localization of nodes, often face noisy distance measurements and uneven distribution of anchor nodes. These factors reduce localization accuracy and increase the computational demands for some nodes. In this paper, an attempt is made to solve these issues by developing a GWO-based localization method for WSNs. In this proposed method, weights are assigned to anchor nodes to minimize the effects of measurement errors and enhance accuracy. Optimizing node selection is also achieved to counterbalance uneven anchor distribution and adapt to changes in network conditions. Thus, with refined distance estimations between nodes, the GWO algorithm achieves better performance compared to traditional anchor selection techniques. Experimental results show that the method proposed here drastically improves localization precision, and hence a promising solution can be used to improvise WSN performance. Enhanced GWO algorithms are highlighted with the potential in creating more reliable and efficient processes for node localization.