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

arXiv: https://arxiv.org/abs/2510.21127 · HTML: https://mlsanity.com/paper/2510.21127/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 10 of 10)

- [Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue](https://mlsanity.com/paper/2309.16709/) (2023)
- [Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing](https://mlsanity.com/paper/2202.12028/) (2022)
- [Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2502.05824/) (2025)
- [Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning](https://mlsanity.com/paper/2404.07450/) (2024)
- [UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2404.07453/) (2024)
- [Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning](https://mlsanity.com/paper/2501.06410/) (2025)
- [Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2505.03230/) (2025)
- [Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking](https://mlsanity.com/paper/2501.15928/) (2025)
- [Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method](https://mlsanity.com/paper/2507.08429/) (2025)
- [Chaos-based reinforcement learning with TD3](https://mlsanity.com/paper/2405.09086/) (2024)

## Abstract (arXiv)

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 and inaccessible environments. As such, wireless rechargeable sensor networks (WRSNs) with mobile charging capabilities offer a promising solution to extend network lifetime. However, WRSNs face critical challenges from the inherent trade-off between maximizing the node survival rates and maximizing charging energy efficiency under dynamic operational conditions. In this paper, we investigate a typical scenario where mobile chargers move and charge the sensor, thereby maintaining the network connectivity while minimizing the energy waste. Specifically, we formulate a multi-objective optimization problem that simultaneously maximizes the network node survival rate and mobile charger energy usage efficiency across multiple time slots, which presents NP-hard computational complexity with long-term temporal dependencies that make traditional optimization approaches ineffective. To address these challenges, we propose an enhanced evolutionary multi-objective deep reinforcement learning algorithm, which integrates a long short-term memory (LSTM)-based policy network for temporal pattern recognition, a multilayer perceptron-based prospective increment model for future state prediction, and a time-varying Pareto policy evaluation method for dynamic preference adaptation. Extensive simulation results demonstrate that the proposed algorithm significantly outperforms existing approaches in balancing node survival rate and energy efficiency while generating diverse Pareto-optimal solutions. Moreover, the LSTM-enhanced policy network converges 25% faster than conventional networks, with the time-varying evaluation method effectively adapting to dynamic conditions.