Accelerate Convergence Rate of Distributed Consensus Algorithm with Optimized Topology

Abstract:

The convergence rate is very important in the distributed consensus problems, especially, for the distributed consensus algorithms based on large-scale complex networks. In order to accelerate the convergence rate of the distributed consensus algorithms, in the paper, we propose an optimized topology model by adding randomly a few shortcuts to the nearest neighbor coupling networks, and the shortcuts follow a normal distribution. By analyses and simulations, the results show that the algebraic connectivity of the new model is bigger than that of the NMW model, and the convergence rate of the distributed consensus based on the new model is higher than that based on the NMW model

• Periodical: Applied Mechanics and Materials (Volumes 511-512)
• Pages: 950-953
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.511-512.950
• Online since: February 2014
• Authors: Huan Xin Peng*, Wen Kai Wang, Bin Liu
• Keywords: Algebraic Connectivity, Complex Network, Distributed Consensus

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