静态业务疏导问题的多目标优化算法研究
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摘要
为解决静态业务疏导问题,综合考虑网络成本、传输时延和业务风险性三个因素,建立静态业务疏导模型,提出静态业务疏导问题的多目标优化算法。算法将各业务的疏导方式作为决策变量,建立最优子种群和次优子种群,利用局部搜索和全局搜索相结合的方法寻找一组最优解。仿真实验证明,该文算法侧重于平衡三个优化目标的性能,以较小的网络成本为代价,大幅降低传输时延和业务风险性。
引文
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[2]Hongyue Zhu,Hui Zang,Keyao Zhu.A novel generic graph model for traffic grooming in heterogeneous WDM mesh networks[J].IEEE/ACM trans on Networking,2003,11(2):285-299.
[3]Wang Yao,Byrav Ramamurthy.A link bundled auxiliary graph model for constrained dynamic traffic grooming in mesh networks[J].IEEE Journal on Selected Areas in Communications,2005,23(8):1542-1555.
[4]杨帆,徐展琦,邱智亮等.光网络中基于组播树的静态业务疏导算法[J],光电子·激光,2011,22(3):372-376.
[5]许齐钦.光网络中业务疏导算法的研究[D].[硕士论文].西安电子科技大学,2011.
[6]Yonggang Li,Longjiang Li.Static Traffic Grooming in WDM Network Using Artificial Immune Algorithm[C].Photonics and Optoelectronics(SOPO),Shanghai,2012:1-4
[7]J.Comellas,R.Martinez,J.Prat.et al.Integrated IP/WDM routing in GMPLS-based optical networks[J].IEEE Network,2003,17(2):22-27.
[8]Leesutthipornchai,P.Charnsripinyo,C.Wattanapongsakorn,et al.Multi-Objective Traffic Grooming in WDM Network using NSGA-II Approach[C].Networked Computing(INC),2010:1-6
[9]曾庆涛,邱雪松,郭少勇,等.基于风险均衡的电力通信业务的路由分配机制[J].电子与信息学报,2013(6),35(6):1318-1324.
[10]吴艾,刘心松,刘丹.基于流量攻击和边失效的复杂网络脆弱特性[J].电子与信息学报,2009(12),31(12):2997-3000.
[11]Srinivas.N,Deb.K.Muilti-objective Optimization Using Nondominated Sorting in Genetic Algorithms[J].Evolutionary Computation.1994(2):221-248.
[12]W right A H.Genetic algorithms for real parameter optimization[J].Foundations of Genetic Algorithms.CA:Morgan Kaufmann,1991:205-218
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[14]公茂果,焦李成,杨咚咚.进化多目标优化算法研究[J].软件学报,2009,20(2):271-289.