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...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。展开更多
保证服务质量的QoS路由(Quality of Service Routing)是网络中解决QoS问题的一项关键技术。QoS路由的主要目标是为接入的业务选择满足服务质量要求的传输路径,同时保证整个网络资源的有效利用。度量参数选择问题、寻路问题和路由信息不...保证服务质量的QoS路由(Quality of Service Routing)是网络中解决QoS问题的一项关键技术。QoS路由的主要目标是为接入的业务选择满足服务质量要求的传输路径,同时保证整个网络资源的有效利用。度量参数选择问题、寻路问题和路由信息不准确问题是QoS路由中的几个主要研究内容。多约束QoS路由算法通常是NPC问题,本文先对QoS路由中的问题进行分类,再对当前研究的一些多约束QoS路由算法进行了归纳与分析。这些算法对于在Internet中实现QoS有着重要的指导意义。展开更多
软件定义网络(Software-Defined Networking,SDN)使得网络功能可以由控制层以软件编程形式实现并下发给数据转发层的交换机执行,提高网络控制灵活性的同时也增加了硬件成本。针对传统网络架构因数据业务多样化导致的网络构建成本高、多...软件定义网络(Software-Defined Networking,SDN)使得网络功能可以由控制层以软件编程形式实现并下发给数据转发层的交换机执行,提高网络控制灵活性的同时也增加了硬件成本。针对传统网络架构因数据业务多样化导致的网络构建成本高、多类型业务(Quality of service,QoS)无法得到有效保障的问题,提出了一种可满足多类型业务QoS的动态自适应路由算法-拉格朗日松弛多约束(Lagrangian Relaxation based Multiple Constains,LRMC)QoS路由算法,在Mininet仿真网络中部署实现。利用Mininet CLI等功能对LRMC多约束QoS路由算法进行仿真对比试验。仿真结果参数表明,上述算法在连通时间收敛性及性能上均表现出明显优势。展开更多
在干扰信号相干的情况下,一般波束形成方法不能准确地对相干信号和期望信号进行到达角(DOA)估计,干扰抑制效果较差。通过对相干信号和加权矢量二次型性能函数的分析,提出了一种相干干扰抑制方法,即基于多约束最小均方(multiple constrai...在干扰信号相干的情况下,一般波束形成方法不能准确地对相干信号和期望信号进行到达角(DOA)估计,干扰抑制效果较差。通过对相干信号和加权矢量二次型性能函数的分析,提出了一种相干干扰抑制方法,即基于多约束最小均方(multiple constrained least mean square,MC-LMS)算法的空域调零抗干扰技术。该方法利用MC-LMS算法迭代计算权值,并对阵列信号自适应加权输出,通过频谱分析可以得到抑制干扰的效果。计算机仿真和工程实现表明该方法在复杂的电磁环境下能对相干干扰进行抑制,并通过抗干扰的数量和抗干扰性能验证了该方法的有效性。展开更多
With a comprehensive consideration of multiple product types, past-sequence-dependent （ p-s-d ） setup times, and deterioration effects constraints in processes of wafer fabrication systems, a novel scheduling model ...With a comprehensive consideration of multiple product types, past-sequence-dependent （ p-s-d ） setup times, and deterioration effects constraints in processes of wafer fabrication systems, a novel scheduling model of multiple orders per job（MOJ） on identical parallel machines was developed and an immune genetic algorithm（IGA） was applied to solving the scheduling problem. A scheduling problem domain was described. A non-linear mathematical programming model was also set up with an objective function of minimizing total weighted earliness-tardlness penalties of the system. On the basis of the mathematical model, IGA was put forward. Based on the genetic algorithm （GA）, the proposed algorithm （IGA） can generate feasible solutions and ensure the diversity of antibodies. In the process of immunization programming, to guarantee the algorithm＇s convergence performance, the modified rule of apparent tardiness cost with setups （ATCS） was presented. Finally, simulation experiments were designed, and the results indicated that the algorithm had good adaptability when the values of the constraints＇ characteristic parameters were changed and it verified the validity of the algorithm.展开更多
Power allocation is an important issue for Cognitive Radio Networks(CRNs),since it needs to consider the Quality of Service(QoS) for Secondary Users(SUs) while maintaining the interference power to Primary User(PU) be...Power allocation is an important issue for Cognitive Radio Networks(CRNs),since it needs to consider the Quality of Service(QoS) for Secondary Users(SUs) while maintaining the interference power to Primary User(PU) below the Interference Temperature(IT) threshold. In this paper, based on Euclidean projection, we propose a distributed power control algorithm with QoS requirements to minimise the total power consumption of SUs under the time-varying channel scenario. Considering the maximum transmit power constraints and the minimum signal to interference plus noise constraints for each SU, together with the IT constraints for each PU, the power allocation problem is transformed into a convex optimization problem without auxiliary variables, and is solved by the Lagrangian dual method with less information exchange.Simulation results demonstrate that the proposed scheme is superior to the Iterative Water-Filling Algorithm(IWFA).展开更多
This paper proposes an effective heuristic algorithm The tree constructed by DDMR has the following characteristics： for dynamic multicast routing with delay-constrained DDMR. （1） multicast tree changes with the dy...This paper proposes an effective heuristic algorithm The tree constructed by DDMR has the following characteristics： for dynamic multicast routing with delay-constrained DDMR. （1） multicast tree changes with the dynamic memberships; （2） the cost of the tree is as small as possible at each node addition/removal event; （3） all of the path delay meet a fixed delay constraint; （4） minimal perturbation to an existing tree. The proposed algorithm is based on “damage” and “usefulness” concepts proposed in previous work, and has a new parameter bf（Balancing Factor） for judging whether or not to rearrange a tree region when membership changes. Mutation operation in Genetic Algorithm （GA） is also employed to find an attached node for a new adding node. Simulation showed that our algorithm performs well and is better than static heuristic algorithms, in term of cost especially.展开更多
In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a N...In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a Novel Adaptive Simulated Annealing （NASA） algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing （AMOSA）. For han-dling multi-objective, NASA makes improverrents in three aspects： sub-iteration search, sub-archive and adaptive search, which effectively strengthen the stability and efficiency of the algorithnm For handling constraints, NASA introduces corresponding solution acceptance criterion. Furtherrrore, NASA has also been applied to optimize TD-LTE network perform-ance by adjusting antenna paranleters; it can achieve better extension and convergence than AMOSA, NS-GAII and MOPSO. Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimi-zation performance.展开更多
文摘针对认知无线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。
文摘保证服务质量的QoS路由(Quality of Service Routing)是网络中解决QoS问题的一项关键技术。QoS路由的主要目标是为接入的业务选择满足服务质量要求的传输路径,同时保证整个网络资源的有效利用。度量参数选择问题、寻路问题和路由信息不准确问题是QoS路由中的几个主要研究内容。多约束QoS路由算法通常是NPC问题,本文先对QoS路由中的问题进行分类,再对当前研究的一些多约束QoS路由算法进行了归纳与分析。这些算法对于在Internet中实现QoS有着重要的指导意义。
文摘软件定义网络(Software-Defined Networking,SDN)使得网络功能可以由控制层以软件编程形式实现并下发给数据转发层的交换机执行,提高网络控制灵活性的同时也增加了硬件成本。针对传统网络架构因数据业务多样化导致的网络构建成本高、多类型业务(Quality of service,QoS)无法得到有效保障的问题,提出了一种可满足多类型业务QoS的动态自适应路由算法-拉格朗日松弛多约束(Lagrangian Relaxation based Multiple Constains,LRMC)QoS路由算法,在Mininet仿真网络中部署实现。利用Mininet CLI等功能对LRMC多约束QoS路由算法进行仿真对比试验。仿真结果参数表明,上述算法在连通时间收敛性及性能上均表现出明显优势。
文摘在干扰信号相干的情况下,一般波束形成方法不能准确地对相干信号和期望信号进行到达角(DOA)估计,干扰抑制效果较差。通过对相干信号和加权矢量二次型性能函数的分析,提出了一种相干干扰抑制方法,即基于多约束最小均方(multiple constrained least mean square,MC-LMS)算法的空域调零抗干扰技术。该方法利用MC-LMS算法迭代计算权值,并对阵列信号自适应加权输出,通过频谱分析可以得到抑制干扰的效果。计算机仿真和工程实现表明该方法在复杂的电磁环境下能对相干干扰进行抑制,并通过抗干扰的数量和抗干扰性能验证了该方法的有效性。
基金National Natural Science Foundations of China(No.61273035,No.71071115)
文摘With a comprehensive consideration of multiple product types, past-sequence-dependent （ p-s-d ） setup times, and deterioration effects constraints in processes of wafer fabrication systems, a novel scheduling model of multiple orders per job（MOJ） on identical parallel machines was developed and an immune genetic algorithm（IGA） was applied to solving the scheduling problem. A scheduling problem domain was described. A non-linear mathematical programming model was also set up with an objective function of minimizing total weighted earliness-tardlness penalties of the system. On the basis of the mathematical model, IGA was put forward. Based on the genetic algorithm （GA）, the proposed algorithm （IGA） can generate feasible solutions and ensure the diversity of antibodies. In the process of immunization programming, to guarantee the algorithm＇s convergence performance, the modified rule of apparent tardiness cost with setups （ATCS） was presented. Finally, simulation experiments were designed, and the results indicated that the algorithm had good adaptability when the values of the constraints＇ characteristic parameters were changed and it verified the validity of the algorithm.
基金supported by the National Natural Science Foundation of China under Grant No.61171079
文摘Power allocation is an important issue for Cognitive Radio Networks(CRNs),since it needs to consider the Quality of Service(QoS) for Secondary Users(SUs) while maintaining the interference power to Primary User(PU) below the Interference Temperature(IT) threshold. In this paper, based on Euclidean projection, we propose a distributed power control algorithm with QoS requirements to minimise the total power consumption of SUs under the time-varying channel scenario. Considering the maximum transmit power constraints and the minimum signal to interference plus noise constraints for each SU, together with the IT constraints for each PU, the power allocation problem is transformed into a convex optimization problem without auxiliary variables, and is solved by the Lagrangian dual method with less information exchange.Simulation results demonstrate that the proposed scheme is superior to the Iterative Water-Filling Algorithm(IWFA).
文摘This paper proposes an effective heuristic algorithm The tree constructed by DDMR has the following characteristics： for dynamic multicast routing with delay-constrained DDMR. （1） multicast tree changes with the dynamic memberships; （2） the cost of the tree is as small as possible at each node addition/removal event; （3） all of the path delay meet a fixed delay constraint; （4） minimal perturbation to an existing tree. The proposed algorithm is based on “damage” and “usefulness” concepts proposed in previous work, and has a new parameter bf（Balancing Factor） for judging whether or not to rearrange a tree region when membership changes. Mutation operation in Genetic Algorithm （GA） is also employed to find an attached node for a new adding node. Simulation showed that our algorithm performs well and is better than static heuristic algorithms, in term of cost especially.
基金supported by the Major National Science & Technology Specific Project of China under Grants No.2010ZX03002-007-02,No.2009ZX03002-002,No.2010ZX03002-002-03
文摘In recent years, sinmlated annealing algo-rithms have been extensively developed and uti-lized to solve nmlti-objective optimization problems. In order to obtain better optimization perfonmnce, this paper proposes a Novel Adaptive Simulated Annealing （NASA） algorithm for constrained multi-objective optimization based on Archived Multi-objective Simulated Annealing （AMOSA）. For han-dling multi-objective, NASA makes improverrents in three aspects： sub-iteration search, sub-archive and adaptive search, which effectively strengthen the stability and efficiency of the algorithnm For handling constraints, NASA introduces corresponding solution acceptance criterion. Furtherrrore, NASA has also been applied to optimize TD-LTE network perform-ance by adjusting antenna paranleters; it can achieve better extension and convergence than AMOSA, NS-GAII and MOPSO. Analytical studies and simulations indicate that the proposed NASA algorithm can play an important role in improving multi-objective optimi-zation performance.
