Genetic algorithm-based dynamic reconfiguration for networked control system

This paper represents a genetic algorithm (GA) based dynamic reconfiguration for networked control systems (NCS) with the objective of minimizing network time-delay. With the development of NCS, it is become more and more important for them to have the minimum time-delay and the ability of dynamic reconfiguration, which can accommodate the changes rapidly, smartly and flexibly. And it is important to find a routing algorithm, which is quicker to reduce the time to update the router and decrease the reconfiguration time as much as possible. In this paper, based on NCS, we discuss the process of GA with specialized encoding, initialization, selection, crossover and mutation. A specialized repair function is used to improve performance. In addition, experiment results are given to illuminate that GA can improve the performance of the NCS.

[1]  Feng-Li Lian,et al.  Network design consideration for distributed control systems , 2002, IEEE Trans. Control. Syst. Technol..

[2]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[3]  Wu Ying,et al.  Job-shop scheduling using genetic algorithm , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[4]  Charles W. Bostian,et al.  Biologically Inspired Cognitive Radio Engine Model Utilizing Distributed Genetic Algorithms for Secure and Robust Wireless Communications and Networking , 2004 .

[5]  Chang Wook Ahn,et al.  A genetic algorithm for shortest path routing problem and the sizing of populations , 2002, IEEE Trans. Evol. Comput..

[6]  Talib S. Hussain,et al.  Adaptive reconfiguration of data networks using genetic algorithms , 2004, Appl. Soft Comput..

[7]  Anup Kumar,et al.  Genetic algorithm based approach for designing computer network topology , 1993, CSC '93.

[8]  Chao-Hsien Chu,et al.  Genetic algorithms for communications network design - an empirical study of the factors that influence performance , 2001, IEEE Trans. Evol. Comput..

[9]  Wu Wei,et al.  A gene-constrained genetic algorithm for solving shortest path problem , 2004, Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004..

[10]  Ali M. S. Zalzala,et al.  Recent developments in evolutionary and genetic algorithms: theory and applications , 1997 .

[11]  Dong-Chul Park,et al.  A neural network based multi-destination routing algorithm for communication network , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[12]  Moshe Sidi,et al.  Topological design of local-area networks using genetic algorithms , 1996, TNET.

[13]  Yong Lu,et al.  A robust stochastic genetic algorithm (StGA) for global numerical optimization , 2004, IEEE Transactions on Evolutionary Computation.