Ant colony algorithm for solving QoS routing problem

Based on the state transition rule, the local updating rule and the global updating rule of ant colony algorithm, we propose an improved ant colony algorithm of the least-cost quality of service (QoS) unicast routing. The algorithm is used for solving the routing problem with delay, delay jitter, bandwidth, and packet loss-constrained. In the simulation, about 52.33% ants find the successful QoS routing, and converge to the best. It is proved that the algorithm is efficient and effective.

[1]  Zhang Jihui,et al.  A Self-Adaptive Ant Colony Algorithm , 2000 .

[2]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[3]  Zhang Subing,et al.  A QoS routing algorithm based on ant algorithm , 2001, ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240).

[4]  Zhang Subing,et al.  Distributed dynamic routing using ant algorithm for telecommunication networks , 2000, WCC 2000 - ICCT 2000. 2000 International Conference on Communication Technology Proceedings (Cat. No.00EX420).

[5]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[6]  Peng Song.  QoS Routing Based on Genetic Algorithm , 2004 .

[7]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[8]  Faouzi Kamoun,et al.  Neural networks for shortest path computation and routing in computer networks , 1993, IEEE Trans. Neural Networks.

[9]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[10]  Li Sheng.  An ant algorithm based VC routing method in ATM networks , 2000 .

[11]  E. D. Taillard,et al.  Ant Systems , 1999 .