针对ASON中双链路失效问题,基于相关链路失效概率(Correlated Link Failure Probability,CLFP)的方法分析传统共享链路保护的可靠性,并提出一种新的支持用户区分可靠性(Diferentiated Reliability,DiR)的共享链路保护算法:SLPDLF-DiR(Shared-Link Protection for Dual Link Failures with DiR).通过实验仿真,与传统的共享链路保护算法(Shared-Link Protection)进行性能比较,结果表明SLPDLF-DiR算法在满足用户区分可靠性要求的前提下,不仅能有效地提供双链路失效保护,而且能充分地利用网络资源.
提出了一种新的粒子群优化方法――融合近邻交互的粒子群优化算法(Particle Swarm Optimization Combined with Near Neighbor Interaction,NNI_PSO).NNI_PSO在PSO算法的速度更新公式中增加了近邻交互部分,并结合"优胜劣汰",引入动态邻域结构和惯性权值非线性变化.近邻交互有利于粒子快速向全局最优移动,"优胜劣汰"有利于维持种群多样性.将NNI_PSO应用于PSO领域五个著名的基准测试函数,并与其它两个著名的PSO改进算法对比,实验结果证明NNI_PSO在收敛速度和解的精度方面均有明显优势.NNI_PSO不仅提高了PSO算法执行的时间性能,而且有效地缓解了早熟收敛问题.
Scheduling jobs on parallel machines to minimize the total tardiness（p//T） is proved to be NP hard.A new ant colony algorithm to deal with p//T（p//T ACO） is addressed, and the computing model of mapping p//T to the ant colony optimization environment is designed.Besides, based on the academic researches on p//T, some new properties used in the evolutionary computation are analyzed and proved.The theoretical analysis and comparative experiments demonstrate that p//T ACO has much better performance and can be used to solve practical large scale problems efficiently.
Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.
This paper proposes a hybrid approach for recognizing human activities from trajectories. First, an improved hidden Markov model （HMM） parameter learning algorithm, HMM-PSO, is proposed, which achieves a better balance between the global and local exploitation by the nonlinear update strategy and repulsion operation. Then, the event probability sequence （EPS） which consists of a series of events is computed to describe the unique characteristic of human activities. The anatysis on EPS indicates that it is robust to the changes in viewing direction and contributes to improving the recognition rate. Finally, the effectiveness of the proposed approach is evaluated by data experiments on current popular datasets.
