Link data optimization based on PSO algorithm in Ethernet protocol

The particle swarm optimization algorithm for optimizing link data in Ethernet protocol can effectively reduce the number of network nodes when the network topology is stable, but when the network topology changes, there will be redundancy. For this reason, this paper proposes a particle swarm inertia weight optimization algorithm, which can adapt to the change of network topology to minimize the energy consumption of link data transmission. Under the condition of without increasing the complexity, this algorithm traverse the selected network nodes again to remove redundancy, and obtain the optimal power distribution of link data transmission. The experimental results show that the improved particle swarm inertia weight optimization algorithm (ZPSO) reduce the consumption of the data transmission power effectively, and improve the reliability of communication, which compared with the traditional particle swarm optimization (PSO).

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Ignacio E. Grossmann,et al.  Decomposition techniques for multistage scheduling problems using mixed-integer and constraint programming methods , 2002 .

[3]  Ahmed Chiheb Ammari,et al.  An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem , 2015, Journal of Intelligent Manufacturing.

[4]  Amin Safari,et al.  A robust PSSs design using PSO in a multi-machine environment , 2010 .

[5]  D. Y. Sha,et al.  A Multi-objective PSO for job-shop scheduling problems , 2009, 2009 International Conference on Computers & Industrial Engineering.

[6]  Guan-zheng Tan,et al.  Hybrid particle swarm optimization with chaotic search for solving integer and mixed integer programming problems , 2014, Journal of Central South University.

[7]  Qiang Zhang,et al.  A spectrum allocation method based on random drift swarm optimization algorithm , 2017, 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN).

[8]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[9]  Andreas Mitschele-Thiel,et al.  Adaptive Discrete Particle Swarm Optimization for Cognitive Radios , 2012, 2012 IEEE International Conference on Communications (ICC).

[10]  Fuad E. Alsaadi,et al.  A switching delayed PSO optimized extreme learning machine for short-term load forecasting , 2017, Neurocomputing.

[11]  J.-d. Decotignie The Many Faces of Industrial Ethernet [Past and Present] , 2009, IEEE Industrial Electronics Magazine.

[12]  K. Premalatha,et al.  Hybrid PSO and GA for Global Maximization , 2009 .

[13]  Michael R. Lyu,et al.  A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training , 2007, Appl. Math. Comput..