Trust Evaluation of Computing Power Network Based on Improved Particle Swarm Neural Network

Chaodong Yu, Geming Xia, Zhaohang Wang

Abstract: In order to satisfy the trust evaluation of efficient cooperative scheduling of computing power in Computing Power Network, we propose a trust evaluation management system with adaptive detection and an efficient lightweight trust evaluation algorithm. In our work, the multi-attribute trust evaluation data combined with the active detection and global trust database are used as samples to train the BP neural network. And the structure and weight coefficient of the neural network are optimized by the improved particle swarm optimization algorithm, so as to reduce the size of neural network and improve its performance. The trust evaluation model in this study effectively improves the detection ratio of malicious state nodes and reduces the detection time.