用自适应混合算法提升物联网传感器定位精度与效率
AdapSCA-PSO: An Adaptive Localization Algorithm with AI-Based Hybrid SCA-PSO for IoT WSNs
- 融合正弦余弦与粒子群算法，动态切换优化搜索策略
- 平均定位误差降低84.97%，迭代次数显著减少
- 适合对定位精度要求高的物联网无线传感网络场景
传感器节点的精准定位是物联网（IoT）实际应用的基本需求。为在多样环境中实现鲁棒定位，本文提出一种混合元启发式定位算法，将擅长全局搜索的正弦余弦算法（SCA）与擅长局部搜索的粒子群优化（PSO）相结合。引入自适应切换模块，动态选择最优算法。同时，针对节点定位问题特性，重新设计了初始化、适应度评估及参数设置。仿真结果表明，在不同传感器节点数量下，相比独立的PSO和未优化的SCAPSO算法，该方法显著减少所需迭代次数，并实现平均定位误差降低84.97%。
原文摘要 · Abstract (English)
The accurate localization of sensor nodes is a fundamental requirement for the practical application of the Internet of Things (IoT). To enable robust localization across diverse environments, this paper proposes a hybrid meta-heuristic localization algorithm. Specifically, the algorithm integrates the Sine Cosine Algorithm (SCA), which is effective in global search, with Particle Swarm Optimization (PSO), which excels at local search. An adaptive switching module is introduced to dynamically select between the two algorithms. Furthermore, the initialization, fitness evaluation, and parameter settings of the algorithm have been specifically redesigned and optimized to address the characteristics of the node localization problem. Simulation results across varying numbers of sensor nodes demonstrate that, compared to standalone PSO and the unoptimized SCAPSO algorithm, the proposed method significantly reduces the number of required iterations and achieves an average localization error reduction of 84.97%.
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