• 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