认知无线Mesh网络中基于WTA的多约束QoS组播路由算法
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摘要
针对认知无线Mesh网络传统的多约束QoS组播路由算法一贯的进行随机初始化种群这一问题,在没有增加智能算法的复杂度的同时,首次将武器-目标分配问题(weapon to target allocation,WTA)应用在群智能算法对初始种群的优化上,基于蚁群算法,将集火射击、分火射击和混合射击的思想加入到对初始种群的设计上,提出一种基于WTA的QoS组播路由优化算法。其目标是满足无线组播业务的QoS约束且不增加算法复杂度的同时,结合蚁群的强鲁棒性和并行性等性能优势。经过实验验证,在网络开销和时延等方面的指标具有很好改善。
In view of the problem that cognitive wireless mesh network's multi-constraint Qo S multicast routing algorithm has always been the traditional random initialization of the population,this paper,on the premise of not increasing complexity of the intelligent algorithm,for the first time applies the Weapon To Target( WTA) allocation to the optimization of initial population in swarm intelligence algorithm. Based on the ant colony algorithm,WTA mixes the concentrated fire,distributed fire and mixed fire ideas to the design of the initial population. An Qo S multicast routing optimization algorithm is proposed based on WTA. The aim is to meet the Qo S constraints of wireless multicast service while not increasing complexity of the algorithm at the same time,in combination with strong robustness of ant colony and performance advantages such as parallelism. The experiment verifies that the algorithm has good improvement in aspects of network cost and delay index.
In view of the problem that cognitive wireless mesh network's multi-constraint Qo S multicast routing algorithm has always been the traditional random initialization of the population,this paper,on the premise of not increasing complexity of the intelligent algorithm,for the first time applies the Weapon To Target( WTA) allocation to the optimization of initial population in swarm intelligence algorithm. Based on the ant colony algorithm,WTA mixes the concentrated fire,distributed fire and mixed fire ideas to the design of the initial population. An Qo S multicast routing optimization algorithm is proposed based on WTA. The aim is to meet the Qo S constraints of wireless multicast service while not increasing complexity of the algorithm at the same time,in combination with strong robustness of ant colony and performance advantages such as parallelism. The experiment verifies that the algorithm has good improvement in aspects of network cost and delay index.
引文
[1]徐慧.粒子群优化算法改进及其在煤层气产能预测中的应用研究[D].北京:中国矿业大学,2013:11-45.
[2]雷翻翻.非线性规划问题的粒子群优化算法研究[D].银川:北方民族大学,2011:09-25.
[3]刘东.粒子群优化算法及其工程应用研究[D].重庆:西南交通大学,2013:36-50.
[4]罗辞勇,卢斌,刘飞.一种求解TSP初始化种群问题的邻域法[J].重庆大学学报,2009,32(11):1311-1315.
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[7]COLORNI A,DORIGO M,MANIEZZO V,et al.Ant system for job-shop scheduling[J].Belgian Journal of Operations Research,Statistics and Computer Science,1994,34(1):39-53.
[8]COSTA D,HERTZA.Ants can colour graphs[J].Journal of the Operational Research Society,1997,48(3):295-305.
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[10]SANTOS L,COUTINHO-RODRIGUES J,CURRENT J R.An improved ant colony optimization based algorithm for the capacitated arc routing problem[J].Transportation Research Part B:Methodological,2010,44(2):246-266.
[11]GAJPAL Y,ABAD P L.Multi-ant colony system(MACS)for a vehicle routingproblem with backhauls[J].European Journal of Operational Research,2009,196(1):102-117.
[12]HU Xiaomin,ZHANG Jun,LI Yun.Orthogonal methods based ant colony search for solving continuous optimization problems[J].Journal of Computer Science&Technology,2008,23(1):2-18.
[13]邢娅浪,何鑫,孙世宇.基于改进蚁群算法的模糊控制器优化设计[J].计算机仿真,2012,29(1):131-134.
[14]陈建良,朱伟兴.蚁群算法优化模糊规则[J].计算机工程与用,2007,43(5):113-115.
[15]STTALE T,HOOS H H.Max-min ant system[J].Future Generation Computer Systems,2000,16(8):889-914.
[16]段海滨.蚁群算法及其在高性能电动仿真转台参数优化中的应用研究[D].南京:南京航空航天大学,2005:9-20.
[17]王健安.基于蚁群优化算法的分布式多约束Qo S路由算法研究[D].长春:长春理工大学,2014:26-28.
[2]雷翻翻.非线性规划问题的粒子群优化算法研究[D].银川:北方民族大学,2011:09-25.
[3]刘东.粒子群优化算法及其工程应用研究[D].重庆:西南交通大学,2013:36-50.
[4]罗辞勇,卢斌,刘飞.一种求解TSP初始化种群问题的邻域法[J].重庆大学学报,2009,32(11):1311-1315.
[5]DORIGO M.Optimization,learning and natural algorithms[D].Milano,Italy:Politecnicodi Milano,1992:97-105.
[6]MANIEZZO V,COLORNIA,DORIGO M.The ant system applied to the quadratic assignment problem[J].IEEE Transactions on Knowledge and Data Engineering,1999,11(5):769-778.
[7]COLORNI A,DORIGO M,MANIEZZO V,et al.Ant system for job-shop scheduling[J].Belgian Journal of Operations Research,Statistics and Computer Science,1994,34(1):39-53.
[8]COSTA D,HERTZA.Ants can colour graphs[J].Journal of the Operational Research Society,1997,48(3):295-305.
[9]BULLNHEIMER B,HARTL R F,STRAUSSC.An improved ant system algorithm for the vehicle routing problem[J].Annals of Operations Research,1999,89:319-328.
[10]SANTOS L,COUTINHO-RODRIGUES J,CURRENT J R.An improved ant colony optimization based algorithm for the capacitated arc routing problem[J].Transportation Research Part B:Methodological,2010,44(2):246-266.
[11]GAJPAL Y,ABAD P L.Multi-ant colony system(MACS)for a vehicle routingproblem with backhauls[J].European Journal of Operational Research,2009,196(1):102-117.
[12]HU Xiaomin,ZHANG Jun,LI Yun.Orthogonal methods based ant colony search for solving continuous optimization problems[J].Journal of Computer Science&Technology,2008,23(1):2-18.
[13]邢娅浪,何鑫,孙世宇.基于改进蚁群算法的模糊控制器优化设计[J].计算机仿真,2012,29(1):131-134.
[14]陈建良,朱伟兴.蚁群算法优化模糊规则[J].计算机工程与用,2007,43(5):113-115.
[15]STTALE T,HOOS H H.Max-min ant system[J].Future Generation Computer Systems,2000,16(8):889-914.
[16]段海滨.蚁群算法及其在高性能电动仿真转台参数优化中的应用研究[D].南京:南京航空航天大学,2005:9-20.
[17]王健安.基于蚁群优化算法的分布式多约束Qo S路由算法研究[D].长春:长春理工大学,2014:26-28.