Abstract
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.
Similar content being viewed by others
Reference
Fogel DB (1995) Evolutionary computation toward a new philosophy of machine intelligence. IEEE Neural Networks Council, IEEE Press, New York
Reuven Elbaum, Moshe Sidi (1996) Topological design of local-area networks using genetic alrorithms. IEEE/ACM Trans Netw 4(5):766–778
Wu Ying, Li Bin (1996) Job-shop scheduling using genetic algorithm. In: Proceeding of the IEEE international conference on systems. Man Cybern 3:994–1999
Rieser Christian James (2004) Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking. Dissertation abstracts international, vol 65–09, section B, p 4748
Chaiyaratana N, Zalzala AMS (1997) Recent developments in evolutionary and genetic algorithms: theory and applications, IEE, Genetic algorithms in engineering systems: innovations and applications, Conference publication no.466, pp 270–277
Anup Kumar, Rakesh M (1993) Genetic algorithm based approach for designing computer network topology. In: Proceedings of ACM computer science conference, pp 358–365
Hsinghua Chou, Chao –Hsien Chu (2001) Genetic algorithms for communications network design—an empirical study of the factors that influence performance, IEEE Trans Evol Comput 5(3):236–249
David Montana, Talib Hussain (2004) Adaptive reconfiguration of data networks using genetic algorithms. Appl Soft comput 4:433–444
Tu Zhengguo (2004) A robust stochastic genetic algorithm(StGA)for global numerical optimization. IEEE Trans Evol Comput 8(5):456–470
Feng-Li Lian, Moyne J, Tilbury D (2002) Network design consideration for distributed control systems. IEEE Trans Control Syst Technol 10(2):297–307
Dong-Chul Park, Seung-Eok Choi (2004) A neural network based multi-destination routing algorithm for communication network. The IEEE international joint conference in 1998, vol 2, May 4–9, pp 1673–1678
Chang Wook Ahn, Ramakrishna RS (2002) A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Trans Evol Comput 6(6):566–579
Wu Wei, Ruan Qiuqi (2004) A gene-constrained genetic algorithm for solving shortest path problem. ICSP 7th international conference on signal processing in 2004, vol 3, pp 2510–2513
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by grants from the National Natural Science Foundation of China (No.60674081 and No.60574088).
Rights and permissions
About this article
Cite this article
chunjie, Z., chunjie, X., hui, C. et al. Genetic algorithm-based dynamic reconfiguration for networked control system. Neural Comput & Applic 17, 153–160 (2008). https://doi.org/10.1007/s00521-007-0096-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-007-0096-8