DocumentCode :
506628
Title :
Adaptive genetic algorithm for multiple QoS anycast routing
Author :
Li, Taoshen ; Zhihui Ge
Author_Institution :
Sch. of Comput., Electron. & Inf., Guangxi Univ., Nanning, China
Volume :
1
fYear :
2009
fDate :
20-22 Nov. 2009
Firstpage :
772
Lastpage :
776
Abstract :
As a new network addressing and routing scheme, anycast has been defined as a standard communication model in IPv6. The multiple QoS constrained anycast routing problem is a nonlinear combination optimization problem, which is proved to be a NP complete problem. This paper studies anycast routing technology with multiple QoS constraints and proposes a multiple QoS anycast routing algorithm based adaptive genetic algorithm. This algorithm uses adaptive probabilities of crossover and mutation over and over again in simple genetic algorithm. Fitness scaling can guarantee the diversity of populations, which is beneficial to find global optimal solution. Simulation results show the efficiency of our algorithm. It can satisfy the constrained condition of multiple QoS, balance network load fairly, and improve the quality of network service.
Keywords :
communication complexity; genetic algorithms; nonlinear programming; quality of service; telecommunication network routing; IPv6; NP complete problem; adaptive genetic algorithm; adaptive probabilities; multiple QoS anycast routing; multiple QoS constrained anycast routing problem; network addressing; network routing; nonlinear combination optimization problem; standard communication model; Admission control; Communication standards; Computer networks; Constraint optimization; Genetic algorithms; Genetic mutations; Network servers; Polynomials; Quality of service; Routing; Quality of Service(QoS); adaptive genetic algorithm; anycast; load balance; routing algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4754-1
Electronic_ISBN :
978-1-4244-4738-1
Type :
conf
DOI :
10.1109/ICICISYS.2009.5358024
Filename :
5358024
Link To Document :
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