基于状态压缩的最长公共上升子序列快速算法

探讨了最长公共上升子序列（LCIS）问题,在前人算法的基础上提出一种高效求解LCIS的动态规划算法。对于LCIS问题,分别使用最长公共子序列（LCS）和最长上升子序列（LIS）相结合的算法、动态规划算法、经过状态压缩的改进动态规划算法进行设计,并对后两种算法进行了实现。设计的状态压缩的动态规划算法,实现了LCIS的快速求解。通过分析这三种算法的时间和空间复杂度,最终提出了时间复杂度为O（mn）、空间复杂度为O（m）或O（n）的基于状态压缩的快速LCIS算法。     

基于改进动态规划的无人机搜寻航迹规划研究

针对传统动态规划算法在无人机航迹规划中存在耗时长以及冗余节点的问题,提出了一种基于多阶段决策的改进动态规划算法.在单向动态规划算法中加入双向策略,结合顺序法和逆序法以减少搜寻空间的状态总数;利用区间单调性和四边形不等式进一步减少每次决策涉及的状态点,得到优化后的状态转移方程,降低了搜寻的时间复杂度.最后,对改进动态规划...     

最经济路线规划算法研究

在城市的公交网络系统中,如果通过乘公交车从点X到点Y,那么如何乘坐(可能需要换乘公交车),使得花费最少?这是Datar和Ranade提出的一个开问题。该文通过对城市公交网络系统的分析,给出了一个解决该问题的时间复杂度为多项式的算法。另外文章还讨论了当找到一条费用最小的乘车路线规划时,如何乘坐公交车使得能最快地到达目的地,并给出了时间复杂度为多项式的算法。     

复合并行机排序问题的启发式算法研究

为有效解决复合并行机排序的极小化最大完成时间问题,提出了分支定界算法和改进的启发式动态规划算法。利用分支定界算法的3个工具:分支模型、边界和优先规则,构建出分支搜索树。按优先规则进行定界搜索,从而减小了问题求解规模。将原始作业转换为虚拟作业,根据Johnson法则,求解出原问题的最优排序。改进的动态规划算法复杂度分析和计算实验表明,这两个算法可靠性高并且可以解决实际问题。     

改进的聚合组播算法

针对组播状态可扩展性问题进行研究, 提出了一种改进的聚合组播算法。该算法在动态在线聚合组播算法的基础上, 确定匹配时可行的聚合树范围, 以减少匹配和计算次数, 降低时间复杂度; 改进最优匹配聚合树的选择算法, 并对网络中聚合树进行定期调整, 以优化聚合树结构, 去掉不必要的中间节点, 提高聚合度, 降低节点转发状态。仿真实验表明, 与同类算法相比, 该算法能够以较低的时间复杂度, 取得较高的聚合度和转发状态降低率, 在并发组播组数目较高和组密度较高时效果更加明显。     

最长递增子序列问题研究

本文采用分治策略和动态规划策略探讨了最长递增子序列问题的两种解法,并分析了算法的计算复杂度。结果表明,本文算法的时间复杂度和空间复杂度分别为O(nlogn)和O(n)。     

方格取数问题的动态规划算法

动态规划算法对很多实际问题的解决是灵活和高效的.首先对方格取数问题进行分析,通过一条路径和两条路径选择的对比分析,得出了该问题的动态规划算法,并对该算法关键部分加以代码实现,最后对该算法的时间和空间复杂度进行分析和讨论,并对复杂度进行优化.试验的结果说明了该算法对于解决该类问题在时间效率上要明显优于贪心算法等一些算法.     

安全培训管理信息系统的研究与设计

本系统利用计算机技术和数据库技术、B/S结构的网络办公协作平台,对特种作业人员培训信息管理。包括：安排培训计划、组织学员报名、安排考试、成绩管理及各种查询统计。系统通过对数据加工、整理、汇总,方便地实现各种查询和统计,提高信息的利用率,加强对特种作业人员培训信息管理。     

一种动态限制搜索区域的最短路径规划算法*

提出一种动态限制搜索区域的最短路径规划算法,它是根据实际道路网络的空间分布特性,动态限制搜索区域,以降低算法的搜索规模,降低算法的时间复杂度和空间复杂度,提高算法的运行效率.实验证明,对于实际城市道路网络结构相对比较规则的最短路径规划,此算法极大地提高了规划的效率.     

无限制二维下料问题的改进动态规划算法

本文给出了一种求解无限制板材下料问题的动态规划解法，对该算法的计算复杂度 进行了分析．并针对算法的特点提出了改进方案．通过理论分析得到改进方案的适用范围， 并描述了这一改进动态规划算法的应用前景．数值实验表明，该算法可以缩简传统动态规划 算法的计算时间和空间，同时得到解的最优值．     

基于Agent的快速公交系统仿真研究

针对目前公交系统仿真模型中存在的建模复杂、数据搜集困难等问题,构建了一个基于agent的快速公交系统仿真模型。将乘客、公交车、信号灯抽象为不同的agent,并引入车辆调度agent和站台管理agent,通过各agent之间的交互来仿真快速公交系统的运行。为了解决个别站点可能出现的"涌现"现象,模型中给出了一种基于动态调整的车辆调度算法,从而减少了乘客的等待时间。实验和分析表明,该模型建模简单,易于理解,不仅可以真实地模拟快速公交系统运行现象,而且对如何充分合理利用公交车资源有一定的借鉴作用。     

Dynamic scheduling of hard real-time tasks and real-time threads

The authors investigate the dynamic scheduling of tasks with well-defined timing constraints. They present a dynamic uniprocessor scheduling algorithm with an O(n log n) worst-case complexity. The preemptive scheduling performed by the algorithm is shown to be of higher efficiency than that of other known algorithms. Furthermore, tasks may be related by precedence constraints, and they may have arbitrary deadlines and start times (which need not equal their arrival times). An experimental evaluation of the algorithm compares its average case behavior to the worst case. An analytic model used for explanation of the experimental results is validated with actual system measurements. The dynamic scheduling algorithm is the basis of a real-time multiprocessor operating system kernel developed in conjunction with this research. Specifically, this algorithm is used at the lowest, threads-based layer of the kernel whenever threads are created     

System level fixed priority energy management algorithm for embedded real time application

Dynamic power management (DPM) and dynamic voltage scaling (DVS) are crucial techniques to reduce the energy consumption in embedded real-time systems. Many previous studies have focused on the energy consumption of the processor or I/O devices. In this paper, we focus on the problem of energy management integrating DVS and DPM techniques for periodic embedded real-time applications with rate monotonic (RM) policy and present a system level fixed priority energy-efficient scheduling (SLFPEES) algorithm. The SLFPEES algorithm consists of I/O device scheduling and job scheduling. I/O device scheduling is based on the dynamic power management with rate monotonic (DPM-RM) policy which puts devices into the sleep state when the idle interval is larger than devices break even time. Job scheduling is based on the RM policy and uses stack resource protocol (SRP) to guarantee exclusive access to the shared resources. For energy efficiency, the SLFPEES algorithm schedules the task with a lower speed and a higher speed. The experimental result shows that the SLFPEES algorithm can yield significantly energy savings with respect to the existing techniques.     

分布式系统TTCAN调度优化算法研究

总线技术的发展给线缆测试仪带来了分布式、信息化、网络化的新需求,且在分布式线缆测试仪工作过程中,测试线路的数目增加也对总线数据通讯的稳定性和通讯效率提出了更高的要求;针对分布式系统在线缆测试中的应用需要,设计并优化了分布式线缆测试仪工作的TTCAN应用层协议和其系统矩阵;对于分布式系统通信中的周期性消息形成的系统矩阵先后采用遗传算法、改进型差分进化算法进行优化,对于其中的非周期性消息采用基于松弛度的动态优先级算法;在MATLAB仿真环境中进行实验,实验结果表明,改进型差分算法比遗传算法能够更快、更稳定地计算出优化矩阵,经调度优化后的TTCAN总线工作时数据传输效率有显著提高;文章通过智能优化算法,有效提高了系统总线的通讯效率和稳定性。     

Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations

Agent-based distributed simulations are confronted with load imbalance problem, which significantly affects simulation performance. Dynamic load balancing can be effective in decreasing simulation execution time and improving simulation performance. The characteristics of multi-agent systems and time synchronization mechanisms make the traditional dynamic load balancing approaches not suitable for dynamic load balancing in agent-based distributed simulations. In this paper, an adaptive dynamic load balancing model in agent-based distributed simulations is proposed. Due to the complexity and huge time consuming for solving the model, a distributed approximate optimized scheduling algorithm with partial information (DAOSAPI) is proposed. It integrates the distributed mode, approximate optimization and agent set scheduling approach. Finally, experiments are conducted to verify the efficiency of the proposed algorithm and the simulation performance under dynamic agent scheduling. The experiments indicate that DAOSPI has the advantage of short execution time in large-scale agent scheduling, and the distributed simulation performance under this dynamic agent scheduling outperforms that under static random agent distribution.     

混合遗传算法在柔性系统动态调度中的应用研究

本文研究了柔性制造系统实时生产环境下的动态调度问题.提出了基于动态数据库技术的动态调 度系统的框架结构.动态数据库中存储着问题的数据结构,包含工件相关类与机器相关类信息.动态数据库能 够随着生产的进行及时进行更新.扰动发生后,遗传算法根据动态数据库所提供的更新后的调度任务数据,快 速产生新的优化调度方案.通过在遗传算法中嵌入约束解决机制确保遗传算法适应约束的能力,从而提高算 法的收敛速度与精度.仿真实验证实了方案的有效性.     

Low-cost task scheduling for distributed-memory machines

In compile-time task scheduling for distributed-memory systems, list scheduling is generally accepted as an attractive approach, since it pairs low cost with good results. List-scheduling algorithms schedule tasks in order of their priority. This priority can be computed either (1) statically, before the scheduling, or (2) dynamically, during the scheduling. In this paper, we show that list scheduling with statically-computed priorities (LSSP) can be performed at a significantly lower cost than existing approaches, without sacrificing performance. Our approach is general, i.e. it can be applied to any LSSP algorithm. The low complexity is achieved by using low-complexity methods for the most time-consuming parts in list-scheduling algorithms, i.e. processor selection and task selection, preserving the criteria used in the original algorithms. We exemplify our method by applying it to the MCP (Modified Critical Path) algorithm. Using an extension of this method, we can also reduce the time complexity of a particular class of list scheduling with dynamic priorities (LSDP) [including algorithms such as DLS (Dynamic Level Scheduling), ETF (Earliest Task First) and ERT (Earliest Ready Task)]. Our results confirm that the modified versions of the list-scheduling algorithms obtain a performance comparable to their original versions, yet at a significantly lower cost. We also show that the modified versions of the list-scheduling algorithms consistently outperform multi-step algorithms, such as DSC-LLB (Dynamic Sequence Clustering with List Load Balancing), which also have higher complexity and clearly outperform algorithms in the same class of complexity, such as CPM (Critical Path Method)     

FlexRay动态段消息调度优化算法

研究FlexRay网络动态段,对汽车的安全、稳定、可靠运行很重要。目前针对FlexRay动态段,设计兼顾最坏响应时间与总线利用率的调度算法相对较少。针对现有研究的不足,本文提出一种类DM算法的启发式调度算法,以减少消息最坏响应时间与提高总线利用率的综合效率为目标,找出最佳的FID分配方案。通过Matlab仿真验证,结果表明该优化调度算法的有效性。     

动态生产调度管理系统的研究与实现

本文基于知识发现提出了一类动态调度算法的框架，在此基础上，基于数据仓库建立 了动态调度管理系统的体系结构，并采用面向对象（Object-Oriented，O-O）技术，分析 和设计了动态调度管理系统，最后阐明了所提方法的优点．     

兑换零钱问题的动态规划算法研究

兑换零钱问题是一个求解组合优化的问题。首先对兑换零钱问题进行了分析,证明了该问题满足动态规划的最优化原理,并给出了其动态规划解法;然后对本算法进行了时间复杂性和空间复杂性分析,得到时间复杂性由通常的动态规划算法的O（Mn2）提高到本算法的O（n3）,空间复杂性由通常的动态规划算法的O（Mn）提高到本算法的O（n2）,因此效率有了较大提高。最后通过实验对算法进行验证,证明了算法的高效性。该算法可以广泛应用于自动售货机。