Journal of System Simulation
Abstract
Abstract: Aiming at the problem of insufficient solution speed and poor generalization of traditional algorithms in large-scale scenarios, this paper intelligently solves the large-scale distributed equipment system preference problem based on deep reinforcement learning. According to the characteristics of distributed equipment system combat, using the complex network to its graph form modeling, and based on the attention mechanism to the equipment between the connecting edge relationship for the characterization, in order to build a distributed equipment system digital simulation environment. Simulation results show that compared with the genetic evolutionary algorithm, the obtained model has obvious advantages in terms of solution time and generalization, which effectively improves the performance of distributed equipment nodes combination selection.
Recommended Citation
Wang, Ziyi; Zhang, Kai; Qian, Dianwei; and Liu, Yuzhen
(2025)
"A DRL⁃based Approach for Distributed Equipment Nodes Selection,"
Journal of System Simulation: Vol. 37:
Iss.
6, Article 20.
DOI: 10.16182/j.issn1004731x.joss.24-0222
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss6/20
First Page
1565
Last Page
1573
CLC
TP391.9
Recommended Citation
Wang Ziyi, Zhang Kai, Qian Dianwei, et al. A DRL⁃based Approach for Distributed Equipment Nodes Selection[J]. Journal of System Simulation, 2025, 37(6): 1565-1573.
DOI
10.16182/j.issn1004731x.joss.24-0222
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