针对当前软件无线电(Software Defined Radio,SDR)应用部署决策系统的应用模型在功率消耗方面考虑的不足,以及现有应用部署算法在处理器数目较大时复杂度高的问题,提出了更为完善的系统模型,并在该模型的基础上提出参数可调整的加窗算...针对当前软件无线电(Software Defined Radio,SDR)应用部署决策系统的应用模型在功率消耗方面考虑的不足,以及现有应用部署算法在处理器数目较大时复杂度高的问题,提出了更为完善的系统模型,并在该模型的基础上提出参数可调整的加窗算法。仿真结果表明,同现有系统模型相比,提出的模型能够保证服务质量,可将系统功耗降低约5%,同时提出的算法兼顾复杂度和性能,可通过调整参数,适当降低复杂度并获得用户可容忍的性能。展开更多
In order to apply overbooking idea in Chinese railway freight industry to improve revenue, a Markov decision process(dynamic programming) model for railway freight reservation was formulated and the overbooking limit ...In order to apply overbooking idea in Chinese railway freight industry to improve revenue, a Markov decision process(dynamic programming) model for railway freight reservation was formulated and the overbooking limit level was proposed as a control policy. However, computing the dynamic programming treatment needs six nested loops and this will be burdensome for real-world problems. To break through the calculation limit, the properties of value function were analyzed and the overbooking protection level was proposed to reduce the calculating quantity. The simulation experiments show that the overbooking protection level for the lower-fare class is higher than that for the higher-fare class, so the overbooking strategy is nested by fare class. Besides, by analyzing the influence on the overbooking strategy of freight arrival probability and cancellation probability, the proposed approach is efficient and also has a good application prospect in reality. Also, compared with the existing reservation(FCFS), the overbooking strategy performs better in the fields of vacancy reduction and revenue improvement.展开更多
In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorith...In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorithm applies the membrane computing theory to quantum bee colony optimization(QBCO),which is an effective discrete optimization algorithm.The global convergence performance of MQBCO is proved by Markov theory,and the validity of MQBCO is verified by testing the classical benchmark functions.Then the proposed MQBCO algorithm is used to solve decision engine problems of cognitive radio system.By hybridizing the QBCO and membrane computing theory,the quantum state and observation state of the quantum bees can be well evolved within the membrane structure.Simulation results for cognitive radio system show that the proposed decision engine method is superior to the traditional intelligent decision engine algorithms in terms of convergence,precision and stability.Simulation experiments under different communication scenarios illustrate that the balance between three objective functions and the adapted parameter configuration is consistent with the weights of three normalized objective functions.展开更多
文摘针对当前软件无线电(Software Defined Radio,SDR)应用部署决策系统的应用模型在功率消耗方面考虑的不足,以及现有应用部署算法在处理器数目较大时复杂度高的问题,提出了更为完善的系统模型,并在该模型的基础上提出参数可调整的加窗算法。仿真结果表明,同现有系统模型相比,提出的模型能够保证服务质量,可将系统功耗降低约5%,同时提出的算法兼顾复杂度和性能,可通过调整参数,适当降低复杂度并获得用户可容忍的性能。
基金Project(2010QZZD021)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2015F024)supported by China Railway Science and Technology Research Development Program
文摘In order to apply overbooking idea in Chinese railway freight industry to improve revenue, a Markov decision process(dynamic programming) model for railway freight reservation was formulated and the overbooking limit level was proposed as a control policy. However, computing the dynamic programming treatment needs six nested loops and this will be burdensome for real-world problems. To break through the calculation limit, the properties of value function were analyzed and the overbooking protection level was proposed to reduce the calculating quantity. The simulation experiments show that the overbooking protection level for the lower-fare class is higher than that for the higher-fare class, so the overbooking strategy is nested by fare class. Besides, by analyzing the influence on the overbooking strategy of freight arrival probability and cancellation probability, the proposed approach is efficient and also has a good application prospect in reality. Also, compared with the existing reservation(FCFS), the overbooking strategy performs better in the fields of vacancy reduction and revenue improvement.
基金Projects(61102106,61102105)supported by the National Natural Science Foundation of ChinaProject(2013M530148)supported by China Postdoctoral Science Foundation+1 种基金Project(HEUCF140809)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(LBH-Z13054)supported by Heilongjiang Postdoctoral Fund,China
文摘In order to effectively solve combinatorial optimization problems,a membrane-inspired quantum bee colony optimization(MQBCO)is proposed for scientific computing and engineering applications.The proposed MQBCO algorithm applies the membrane computing theory to quantum bee colony optimization(QBCO),which is an effective discrete optimization algorithm.The global convergence performance of MQBCO is proved by Markov theory,and the validity of MQBCO is verified by testing the classical benchmark functions.Then the proposed MQBCO algorithm is used to solve decision engine problems of cognitive radio system.By hybridizing the QBCO and membrane computing theory,the quantum state and observation state of the quantum bees can be well evolved within the membrane structure.Simulation results for cognitive radio system show that the proposed decision engine method is superior to the traditional intelligent decision engine algorithms in terms of convergence,precision and stability.Simulation experiments under different communication scenarios illustrate that the balance between three objective functions and the adapted parameter configuration is consistent with the weights of three normalized objective functions.
