单源最短路问题高效算法探究

单源最短路问题是算法研究中由来已久的一个问题,在算法领域早期已经得到了较好的解决,但是在应用计算机语言实现的过程中往往不够优化．导致较高的时间复杂度和空间复杂度。从原始的迪杰斯特拉算法入手,进行透彻分析,在算法思想和实现方式上提出一种全面优化的算法方案,并给出了核心代码。实现过程中使用了堆的数据结构,并在具体的实现过程中进行灵活的优化。经过理论的算法复杂度分析,以及实际的数据测试,都证明全新优化后地单源最短路算法计算耗时非常少,空间复杂度也得到很大程度的降低．应用价值更强。     

基于GIS的救护车辆最短路径算法

基于地理信息系统(GIS) 台,利用经典的单源最短路径算法--Dijkstra算法,对其进行了最小堆结构和邻接表存储模型优化.程序仿真结果表明,优化后的结果比经典算法在时间复杂度和空间复杂度上都有所降低,在救护车辆最短路径选择中有一定的实际价值.     

一种基于桶结构的单源最短路径算法

以单源最短路径为主的最优路径问题是众多社会应用领域内选择最优问题的基础。本文分析了不同实现技术求解单源最短路径问题的算法,结合基于标记设定的Dijkstra算法和基于标记修正的BFM算法的思想,提出了一种基于桶结构的单源最短路径算法。实验结果表明,该算法与前两种算法相比,具有好的运行时间复杂度和可并行性。     

求解多段图问题的并行动态规划算法

多段图问题是一类特殊的单源最短路径问题。在串行动态规划算法的两种实现方法的基础上,根据图中顶点的编号,提出两种在集群环境下进行任务分割的并行化求解方法,并使用MPI进行实现。实验结果表明,所提出的算法具有较高的加速比和较低的通信复杂度、时间复杂度。算法不限于某种结构的集群,通用性强。     

聚类方法在公共设施选址中的应用研究

对空间数据挖掘聚类技术及其在公共设施选址方面的传统应用方法进行了综述,分析了传统应用方法中有待解决的关键问题,对空间距离代价的表示问题和传统方法的算法时间复杂度进行了初步探讨,运用模拟退火算法和图论对传统方法进行了改进,实现了算法时间复杂度的降低和聚类结果的优化。     

快速数据包分流算法研究

基于“流”的数据包分类算法已经在第四层交换等领域中得到了应用,该类算法的特点是流表的容量大,流表的更新速度较快.“快速的数据包分流算法”采用了散列算法的基本思想,并引入了流的局部性原理来加速散列查找的过程,用软件对该算法进行了仿真测试,并在最后从时间复杂度和空间复杂度两个方面对其进行了性能分析.实验结果表明,该算法具有良好的时间复杂度和空间复杂度,可以实现快速的分流.     

最短路算法在生产最优化中的应用

给出了一种基于Djikstra最短路算法的实现,该算法实现可以求得有限权图中任一点到其他所有点的最短路径及相应的距离,并清晰完整地表现求解过程及所得结果。生产领域中的一些多阶段优化决策问题可以转化为最短路径问题,由所给出的算法实现来解决这些多阶段优化问题,可以一次求得各不同阶段内的最优策略。以求解设备更新问题和原料选用问题为例,显示了这一算法实现可以完全而简捷地解决多阶段优化决策问题的特点,是最短路算法在生产过程最优化领域的有效运用。     

无回路网络中最短路问题的高效算法

无回路网络是一类重要的网络,给出在无回路网络中求解最短路树形图和任意顶点对间最短路的高效算法。该算法将顶点进行重新编号,结合广度优先探索法,从源顶点出发依次搜索每个顶点的所有出弧,并在弧的头部进行权值变换操作,可以得到最短路树形图和任意顶点对间最短路,算法复杂度分别为O(m)和O(m(n-m1/2))。该算法思想简便、复杂度低、易于操作。 关键词：     

A*算法在车辆导航系统中的应用研究

在十分复杂的交通地形图中,通过分析完备性、最优性、时间复杂度、空间复杂度性能问题,使用当代流行的智能语言-Mathematica,提出三组不同估价函数对基于启发式搜索的A*算法进行优化,从而实现了车辆导航的高效率化.实验结果表明:优化后的估价函数是切实有效的,在应用导航系统中得到了较满意的选路方案.     

基于链表的Dijkstra算法优化研究

迪杰斯特拉算法是图论中计算最短路径的经典算法,但在实际使用中该算法耗费大量的计算时间和存储空间。通过对传统迪杰斯特拉算法的深入分析,在计算时间和存储空间上对该算法提出了一种新的优化方案,并给出了优化后的详细算法。改进算法从消除冗余计算和冗余存储入手,采用链表数组作为存储结构。经算法复杂度分析,优化后的迪杰斯特拉算法在求解最短路径问题时在时间和空间复杂度上都有明显的提高。该优化算法操作性强,具有一定的实用价值。     

CS-ACELP语音编码算法的优化及其在DSP上的实现

针对CS-ACELP语音编码算法编码复杂度较高、DSP实时实现比较困难的问题,提出了一种可降低CS-ACELP语音编码算法复杂度的优化方法,分析了CS-ACELP语音编码算法原理,详细介绍了优化的CS-ACELP语音编码算法从固定码本搜索上降低算法复杂度的实现,并给出了在16位定点DSP芯片TMS320VC5402上实现CS-ACELP语音编解码方案的硬件及软件设计。实验结果表明,优化的CS-ACELP语音编码算法降低了运算复杂度,提高了运行速度,重建的语音符合标准的编解码要求。     

基于VLIW的汇编级FDCT和扫描量化优化算法

在视频处理领域以及M EPG2/4相关领域中FDCT以及扫描量化操作的执行效率已经成为大家共同关注的问题,算法优化日益成为热点。文章首先对两个经典的FD CT和扫描量化算法进行了分析,指出了算法的不足之处并给出了解决办法,以TM1300为例介绍了VLIW结构的特性,研究了基于D SP芯片的优化方法,并应用这些方法对M PEG4的FD CT、量化和扫描进行了汇编级的量化优化,最后利用仿真板提供的性能分析工具对汇编级优化的效果和C语言级优化的效果进行了比较,汇编级优化所需指令周期约为C语言级优化周期的60%。     

A genetic algorithm with genes-association recognition for flowshop scheduling problems

In this paper, we consider a flowshop scheduling problem with a special blocking RCb (Release when Completing Blocking). This flexible production system is prevalent in some industrial environments. Genetic algorithms are first proposed for solving these flowshop problems and different initial populations have been tested to find which is best adapted. Then, a method is proposed for further improving genetic algorithm found solutions, which consists in marking out recurrent genes association occurrences in an already genetic algorithm optimized population. This idea directly follows Holland’s first statement about nature observations. Here, proposed idea is that populations well adapted to a problem have an adapted genetic code with common properties. We propose to mark out these properties in available genetic code to further improve genetic algorithm efficiency. Implementation of this method is presented and obtained results on flowshop scheduling problems are discussed.     

EXIT analysis of M-algorithm based MIMO detectors and LDPC code design optimization

This paper investigates joint design and optimization of both low density parity check (LDPC) codes and M-algorithm based detectors including iterative tree search (ITS) and soft-output M-algorithm (SOMA) in multiple-input multiple-output (MIMO) systems via the tool of extrinsic information transfer (EXIT) charts. First, we present EXIT analysis for ITS and SOMA. We indicate that the extrinsic information transfer curves of ITS obtained by Monte Carlo simulations based on output log-likelihood rations are not true EXIT curves, and the explanation for such a phenomenon is given, while for SOMA, the true EXIT curves can be computed, enabling the code design. Then, we propose a new design rule and method for LDPC code degree profile optimization in MIMO systems. The algorithm can make the EXIT curves of the inner decoder and outer decoder match each other properly, and can easily attain the desired code with the target rate. Also, it can transform the optimization problem into a linear one, which is computationally simple. The significance of the proposed optimization approach is validated by the simulation results that the optimized codes perform much better than standard non-optimized ones when used together with SOMA detector.     

Study on joint Bayesian model selection and parameter estimation method of GTD model

The Bayesian method is applied to the joint model selection and parameter estimation problem of the GTD model. An algorithm based on RJ-MCMC is designed. This algorithm not only improves the model order selection and parameter estimation accuracy by exploiting the priori information of the GTD model, but also solves the mixed parameter estimation problem of the GTD model properly. Its performance is tested using numerical simulations and data generated by electromagnetic code. It is shown that it gives good model order selection and parameter estimation results, especially for low SNR, closely-spaced components and short data situations.     

FASTEST: a practical low-complexity algorithm for compile-timeassignment of parallel programs to multiprocessors

In the area of parallelizing compilers, considerable research has been carried out on data dependency analysis, parallelism extraction, as well as program and data partitioning. However, designing a practical, low complexity scheduling algorithm without sacrificing performance remains a challenging problem. A variety of heuristics have been proposed to generate efficient solutions but they take prohibitively long execution times for moderate size or large problems. In this paper, we propose an algorithm called FASTEST (Fast Assignment and Scheduling of Tasks using an Efficient Search Technique) that has O(e) time complexity, where e is the number of edges in the task graph. The algorithm first generates an initial solution in a short time and then refines it by using a simple but robust random neighborhood search. We have also parallelized the search to further lower the time complexity. We are using the algorithm in a prototype automatic parallelization and scheduling tool which compiles sequential code and generates parallel code optimized with judicious scheduling. The proposed algorithm is evaluated with several application programs and outperforms a number of previous algorithms by generating parallelized code with shorter execution times, while taking dramatically shorter scheduling times. The FASTEST algorithm generates optimal solutions for a majority of the test cases and close-to-optimal solutions for the rest     

适用于无向网络的动态Dijkstra算法优化

网络拓扑发生变化时,利用静态Dijkstra算法重新计算最短路径树(SPT)会造成冗余计算。动态Dijkstra算法解决了这个问题,但目前动态算法一般是基于有向网络模型进行的研究。在已有的动态Dijkstra算法基础上,提出适用于无向网络的动态Dijkstra算法。算法主要解决了在无向网络中如何确定待更新节点的问题,对网络中的一条边权值增大、减小的处理方法进行了详细描述,并对已有的算法的筛选机制进行了优化。为了验证算法的正确性,用仿真实验实现了该算法并与静态算法进行性能比较。实验结果表明,新算法更能提高节点更新的时间效率。     

Solving the Identifying Code Problem by a Genetic Algorithm

Identifying code is a concept in information theory and can be applied to problems of fault detection and location detection. In this paper, by assigning cost to every code word, we propose an optimization problem to find an identifying code with minimum cost and formulate the problem by an integer program. We generalize the results to the robust identifying code problem, which is proposed for poor environments. A tailored genetic algorithm is provided to solve the problem, and the experimental result shows that it is competitive for large-scale problems     

一种基于比特量化的GPS并行码捕获算法

针对GPS L1信号的软件接收机(SDR)的快速捕获问题,设计了GPS信号并行码相位搜索捕获的优化算法.首先分析了FFT并行码相位搜索捕获的理论模型,由本地C/A码功率谱频谱分布特点,使用前半频域范围内的信息,进行算法的初步优化;在此基础上采用比特量化方法量化数据信号,使单个计算机储存单元存储多路二进制量化数据,实现对多路数据的并行处理.实验证明改进的并行码相位搜索捕获算法极大地提高了捕获速度,是改进前的5倍,尽管有较少信号强度的衰减,但不影响捕获的正确性,实现了GPS信号的快速捕获.