基于多策略融合改进蛇优化算法的WSN覆盖优化研究

李胜锋; 田彦山; 马旭; 张芳琴; 杨金米; 马甜甜; 罗晓丽

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基于多策略融合改进蛇优化算法的WSN覆盖优化研究

更新时间：2026-03-03

- 基于多策略融合改进蛇优化算法的WSN覆盖优化研究
- WSN Coverage Optimization Based on an Improved Snake Optimizer with Multi-Strategy Integration
- 物联网学报 2026年
- 作者机构：
  
  宁夏师范大学数学与计算机科学学院，宁夏固原 756099
- 作者简介：
  
  田彦山，tianysh@nxnu.edu.cn
- 基金信息：
  
  国家自然科学基金地区科学基金项目(62566048);宁夏自治区重点研发计划(2023BEG02072);宁夏自然科学基金项目(2025AAC030615)
- DOI：
  中图分类号：
- 收稿：2025-10-12，
  
  修回：2025-11-07，
  
  录用：2026-02-09，
- 稿件说明：
移动端阅览
李胜锋, 田彦山, 马旭, 等. 基于多策略融合改进蛇优化算法的WSN覆盖优化研究[J/OL]. 物联网学报, 2026.

LI Shengfeng, TIAN Yanshan, MA Xu, et al. WSN Coverage Optimization Based on an Improved Snake Optimizer with Multi-Strategy Integration[J/OL]. Chinese Journal on Internet of Things, 2026.
李胜锋, 田彦山, 马旭, 等. 基于多策略融合改进蛇优化算法的WSN覆盖优化研究[J/OL]. 物联网学报, 2026. DOI：

LI Shengfeng, TIAN Yanshan, MA Xu, et al. WSN Coverage Optimization Based on an Improved Snake Optimizer with Multi-Strategy Integration[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

为优化无线传感器网络中因节点随机部署而产生的覆盖空洞、分布不均及能效低下等问题，提出了一种多策略融合的改进蛇优化算法。首先，采用Tent混沌映射与改进版对立学习策略生成高质量初始种群，以提升初始解的质量。其次，设计了随机-精英交替引导机制，以平衡并增强算法的全局探索能力。随后，在开发阶段引入了量子竞争纠缠与遗传表观调控机制，旨在增强种群多样性并加速收敛。此外，采用动态适应度排名策略以自适应调整选择压力。仿真实验结果表明，与对比算法相比，该新算法在网络覆盖率与节点分布均匀性方面均展现出更优的性能，为解决WSN覆盖优化问题提供了一种有效的解决方案。

Abstract

To address the issues of coverage holes

uneven distribution

and low energy efficiency caused by random node deployment in Wireless Sensor Networks

an improved Snake Optimizer integrating multiple strategies was proposed. Firstly

Tent chaotic mapping and an improved opposition-based learning strategy were employed to generate a high-quality initial population. Secondly

a random-elite alternating guidance mechanism was designed to enhance the exploration capability of the algorithm. Subsequently

quantum competitive entanglement and genetic epigenetic regulation mechanisms were introduced during the exploitation phase to increase population diversity and improve convergence. Furthermore

a dynamic fitness ranking strategy was adopted to adaptively adjust the selection pressure. Simulation results demonstrated that the proposed algorithm outperformed other compared algorithms in terms of both coverage rate and distribution uniformity

thereby providing an effective solution for WSN coverage optimization.

关键词

Keywords

references

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