Year: 2025
Type: article
Abstract: Traditional methods have limitations regarding efficient collaboration and dynamic response in complex dynamic environments. Although existing swarm intelligence control methods possess certain adaptive optimization capabilities, they still face challenges in individual and global collaborative optimization and adaptability. To address this challenge, a swarm intelligence control design method based on double-layer deep reinforcement learning (D-DRL) is proposed. This method uses a double architecture where the inner layer is responsible for dynamic decision-making and behavior optimization, and the outer layer manages resource allocation and strategy optimization. The dynamic interaction between the inner and outer layers, coupled with multi-level collaborative optimization, enhances the system’s adaptability and operating performance. The results of the unmanned aerial vehicle (UAV) swarm case study show that our method achieves effective convergence and outperforms existing swarm intelligence control approaches. Specifically, it simultaneously optimizes energy efficiency and task completion amount with a superior performance. This improvement significantly enhances the comprehensive task effectiveness of the swarm.
Source: Applied Sciences
DOI: 10.3390/app15084337
Authors: Xiangpei Yan, Guorui Yu, Guoke Huang, Ruchuan Zhou, Liu Hao
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409442416