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

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

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Chenglong BanCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, China.

Xiang LiuCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, China.

Min TianCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, China. tm_inf@shzu.edu.cn.

Zhonghua LuCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, China.

Ruoxue XiangCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832000, China.

Jie ZhouCollege of Information Science and Technology, Shihezi University, Shihezi, 832000, China.

Funding

National Natural Science Foundation of China No. 61962053New Generation of Artificial Intelligence National Scientific and Technological Major Special Project No.2022ZD0115800Research Initiation Fund for High-level Talents of Shihezi University No. RCZK2018C39

6 · The paper itself

Abstract

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.

Identifiers

PMID40594220

DOI10.1038/s41598-025-04213-y

PMCPMC12216883

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.