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<p>Soil Temperature Wireless Sensor Networks (STWSNs) are essential for optimizing agricultural practices by providing real-time soil temperature data in cotton fields. However, current heuristic algorithms face limitations in achieving high coverage with minimal sensor nodes. This paper introduces an Adaptive Chaotic Gaussian Lens Snake Optimization Algorithm (ACGLSOA) to address this issue. The proposed ACGLSOA integrates two novel adaptive factors to enhance local search capabilities and incorporates advanced chaos operators to refine initial solutions. Additionally, the algorithm employs an improved Gaussian operator and a lens reflection mechanism to expand the search space, thereby enhancing global search performance. Experimental results indicate that ACGLSOA achieves a network coverage of 98.91% for STWSNs, with a node utilization efficiency of 73.8%. Compared to the Snake Optimizer (SO), Artificial Bee Colony Algorithm (ABC), RIME Optimization Algorithm (RIME), and Particle Swarm Optimization Algorithm (PSO), ACGLSOA improves STWSN coverage by 9.74%, 8.24%, 14.45%, and 29.68%, respectively, and enhances node utilization efficiency by 7.27%, 6.15%, 10.78%, and 22.13%, respectively.</p>

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Adaptive chaotic gaussian lens snake optimization algorithm for improved cotton field sensor coverage and utilization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Chenglong Ban,
  • Xiang Liu,
  • Min Tian,
  • Zhonghua Lu,
  • Ruoxue Xiang,
  • Jie Zhou

摘要

Soil Temperature Wireless Sensor Networks (STWSNs) are essential for optimizing agricultural practices by providing real-time soil temperature data in cotton fields. However, current heuristic algorithms face limitations in achieving high coverage with minimal sensor nodes. This paper introduces an Adaptive Chaotic Gaussian Lens Snake Optimization Algorithm (ACGLSOA) to address this issue. The proposed ACGLSOA integrates two novel adaptive factors to enhance local search capabilities and incorporates advanced chaos operators to refine initial solutions. Additionally, the algorithm employs an improved Gaussian operator and a lens reflection mechanism to expand the search space, thereby enhancing global search performance. Experimental results indicate that ACGLSOA achieves a network coverage of 98.91% for STWSNs, with a node utilization efficiency of 73.8%. Compared to the Snake Optimizer (SO), Artificial Bee Colony Algorithm (ABC), RIME Optimization Algorithm (RIME), and Particle Swarm Optimization Algorithm (PSO), ACGLSOA improves STWSN coverage by 9.74%, 8.24%, 14.45%, and 29.68%, respectively, and enhances node utilization efficiency by 7.27%, 6.15%, 10.78%, and 22.13%, respectively.