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