English(EN) Enhanced Evolutionary Multi-Objective Deep Reinforcement Learning for Reliable and Efficient Wireless Rechargeable Sensor Networks

AI算法提升无线传感器网络效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-17 04:00

一篇研究论文提出了一种增强进化多目标深度强化学习算法，以应对无线可充电传感器网络（WRSNs）中的挑战。该算法旨在平衡节点存活率与充电能量效率，这两者之间常常存在冲突。它集成了基于LSTM的策略网络用于时序模式识别，以及多层感知机用于未来状态预测，在模拟中表现优于现有方法。 AI

排序理由该集群包含一篇已撤回的学术论文，详细介绍了一种新颖的算法。[lever_c_research降级：ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Bowei Tong, Hui Kang, Jiahui Li, Geng Sun, Jiacheng Wang, Yaoqi Yang, Bo Xu, Dusit Niyato · 2026-06-17 04:00

Enhanced Evolutionary Multi-Objective Deep Reinforcement Learning for Reliable and Efficient Wireless Rechargeable Sensor Networks

arXiv:2510.21127v2 Announce Type: replace-cross Abstract: Despite rapid advancements in sensor networks, conventional battery-powered sensor networks suffer from limited operational lifespans and frequent maintenance requirements that severely constrain their deployment in remote…