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整合多代理强化学习与黄金子优化,用于预测无线传感器网络的平均定位错误

K Lakshmi Prabha1, Hanan Abdullah Mengash2, Hamed Alqahtani3

  • 1Department of Electronics and Communication Engineering, Chennai Institute of Technology, Chennai, 600069, Tamil Nadu, India. lakshmisslp@gmail.com.

Scientific reports
|July 24, 2025
PubMed
概括

本研究介绍了一种优化的多代理强化学习 (MARL) 框架与黄金子优化 (GJO),以提高无线传感器网络 (WSN) 定位精度. 这种新的方法可以动态调整参数,在具有挑战性的环境中显著减少本地化错误.

关键词:
黄金杰克的优化优化局部化错误 局部化的错误多代理强化学习算法多代理强化学习算法预测. 预测. 这是预测. 错误最小化 错误最小化无线传感器网络无线传感器网络

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