基于QoS约束的多播路由研究

该文给出了多播路由的定义和多播树的分类,描述了一种适用于研究QoS多播路由的网络模型,对多播路由算法进行了分类,介绍了典型的基于QoS约束的多播路由协议和算法,对每个算法的性能特性和适应范围进行了客观的评价,提供了QoS多播路由算法复杂度的比较,对QoS多播路由技术的进一步研究进行了展望。     

基于斐波那契序列的多播算法

该文提出了一种基于斐波那契序列的多播算法 ,并在 log P模型 [1 ] 下对算法的性能进行了分析 .log P模型是一种广泛使用的并行计算模型 ,它利用 L,o,g,P四个参数来分别表示发送一条消息的等待时间或最大延迟、处理器的开销、源结点发送消息的时间间隔、处理器 /存储器模块数 .在 log P模型下 ,该文所述的基于斐波那契序列的多播算法的时间复杂度为 0 .72 0 2 2· log2 K· (g m ax{ L 2· o,2· g} ) ,而传统的采用均匀二分的多播算法时间复杂度为 log2 K· (L 2· o) ,其中 K为结点数 .当 g 0 .3884· (L 2· o)时 ,基于斐波那契序列的多播算法性能将优于采用均匀二分策略的多播算法 .由于实际情况中 L 2 o g,因此 ,基于斐波那契序列的多播算法性能更优 .实验结果也验证了这一结论     

最小延迟的应用层多播树算法研究

在构建高效的应用层多播树过程中,许多端系统需要时间处理发送和转发数据,因此求解"最小延迟生成树"的问题必须考虑节点的处理延迟.本文分析了两类应用层多播路由模型:基于优化延迟的MRDL模型和基于负载平衡的LRRB模型,并证明都是NP难问题,且提出相应的两类启发式近似算法:MRDL-H算法和LRRB-H算法.最后通过模拟实验说明了所提出算法的高效性、健壮性.     

基于多播的线性网络编码

网络编码是网络中间节点对网络信息流进行存储转发的基础上实行编码操作,从而达到提高网络吞吐量,改善网络均衡负载,节省网络带宽等效果.网络编码在国内外已成为研究热点.本文在介绍网络编码基本原理的基础上,分析了线性网络编码的构造算法,包括指数时间算法,多项式时间算法和随机网络编码构造算法,最后对网络编码的研究趋势进行了展望.     

一种基于遗传算法的QoS多播路由算法

深入研究基于遗传算法的QoS多播路由算法,建立支持QoS的多播路由模型.对已有的QoS多播路由算法进行优化,提出适用于下一代网络的基于遗传算法的QoS多播路由算法.采用定长的染色体编码和预处理机制降低算法复杂度.仿真试验表明,该算法收敛速度快,可靠性高,能够更好地满足多播业务的需要.     

基于网络编码的多播路由算法研究

网络编码的提出在一定程度上提高了多播通信的传输性能。简要总结了网络编码多播传输理论的研究进展,并对网络编码多播路由问题进行了研究。分析了已有算法的优势及不足,同时考虑影响资源消耗的因素,文中提出了一种基于最短路径和最大共享链路集的网络编码多播路由算法。通过在随机网络拓扑模型下的性能仿真实验分析,发现与传统的IP最短路多播和约简网络下网络编码多播路由算法相比,该算法可显著减少网络带宽资源消耗,同时能够有效均衡链路负载。     

多播路由KPP算法的改进

论文提出一种满足端到端时延限制的多播路由算法。该算法参考KPP[7]算法,在构造多播路由树的过程中动态调整路径的选取,使尽可能地共享网络中的链路,并对所构造的多播树进行进一步的调整优化,最后得到一棵低代价的满足端到端时延限制的多播路由树。论文通过对KPP算法进行分析发现KPP算法思想忽略了对转发节点的处理,而且在两节点间路径的选取过程中仅仅选取最佳路径,这就导致了对边稠密的图,KPP算法存在缺陷。算法基于上述缺陷完善了KPP算法,在复杂的网络图中应用该算法比KPP算法更加有效,实验模拟表明该算法构造的多播树与KPP算法构造的多播树相比能优化9%到10%。     

基于多QoS约束的多播路由算法研究

论文讨论了具有延迟、带宽和低代价等多QoS约束的多播路由算法,提出了适应于研究QoS多播路由的网络模型,并给出了一种具有多QoS约束的动态多播路由算法,分析了算法的复杂度。仿真实验证明,该算法是稳定有效的。它能够在满足多约束的情况下,使多播树的代价优化。     

基于多目标优化的应用层多播路由方案

针对网络中的多播路由问题,提出了同时兼顾时延、平衡和网络层业务量均衡多目标优化的应用层多播路由优化模型.该模型综合了网络层多播的高效率和应用层多播的灵活性的优点.通过将混沌免疫进化算法用于解决应用层多播路由多目标优化问题,最终产生一组最优非劣解集.该算法能够较好地保持种群的多样性,且收敛速度快,搜索能力较强,可以克服采用单目标路由优化方法的不足.最后仿真实验结果表明了该算法的有效性.     

一种新的QoS覆盖多播路由协议的研究

研究了QoS覆盖多播路由问题,对度约束模型进行扩展,提出了一个新的支持QoS路南的覆盖多播网络模型．基于此模型,提出丁一个新的QoS覆盖多播路南协议QOS覆盖多播树协议（QOMTP）．该协议采用分布式和树优先的策略,使多播组成员之间能自组织地构建一棵基于源的、满足QoS约束的覆盖多播树．该协议采用了一种新的启发式局部优化算法,通过调节启发因子,能灵活地在延时和带宽之间进行均衡．仿真实验表明,采用按请求带宽转发媒体流,并选择适当的启发因子,QOMTP协议能减少多播树上节点的网络资源占用越,同时获得较高的节点接纳率和较好的动态适心性,从而证明了该协议机制及其算法的有效性．     

基于协同过滤的连续黑箱优化问题元启发算法选择

算法选择(AS)问题旨在为给定问题在算法集合中选择最佳算法.随着优化算法的不断提出,算法选择问题是优化领域亟待解决的问题.提出基于聚类的元启发算法五星评价体系,将算法性能指标映射至整数评价以减小评价空间.通过测试24种常见优化算法与4种最新CEC大赛优胜算法在219种、3000多个标准测试问题上的性能,得到评价矩阵.将评价矩阵作为训练数据,使用协同过滤(CF)算法建立算法评价的预测模型.使用该模型预测算法集内的所有算法在新问题上的评价,结果显示所提出方法预测精度较高,超过90%的预测最佳算法为最终可行算法.敏感性分析显示,该方法在先验信息有限的情况下仍可以保持较高的预测精度.     

有约束竞争选址问题的降阶回溯算法

有约束竞争选址问题是组合优化中一个经典的NP-hard问题,现有算法研究该问题时或是无法求得最优解或是求解速度慢.针对现有算法的缺点,首先在这个经典问题的基础上进行修改,构建了一个新的数学模型;接着对该模型的数学性质进行研究,并在数学性质的基础上提出了上下界算法和降阶子算法对问题进行降阶,达到了缩减问题搜索解空间的目的,降阶的过程中既有单个的降阶,也有成批的降阶;然后在前面的基础上设计了一个回溯子算法来求解问题的最优解;最后通过两个示例分析更清楚地阐述该算法的原理,结果证明该算法可以较快求得最优解.     

Multiobjective model for solving resource‐leveling problem with discounted cash flows

Nowadays, executers are struggling to improve the economic and scheduling situation of projects. Construction scheduling techniques often produce schedules that cause undesirable resource fluctuations that are inefficient and costly to implement on site. The objective of the resource‐leveling problem is to reduce resource fluctuation related costs (hiring and firing costs) without violating the project deadline. In this article, minimizing the discounted costs of resource fluctuations and minimizing the project makespan are considered in a multiobjective model. The problem is formulated as an integer nonlinear programming model, and since the optimization problem is NP‐hard, we propose multiobjective evolutionary algorithms, namely nondominated sorting genetic algorithm‐II (NSGA‐II), strength Pareto evolutionary algorithm‐II (SPEA‐II), and multiobjective particle swarm optimization (MOPSO) to solve our suggested model. To evaluate the performance of the algorithms, experimental performance analysis on various instances is presented. Furthermore, in order to study the performance of these algorithms, three criteria are proposed and compared with each other to demonstrate the strengths of each applied algorithm. To validate the results obtained for the suggested model, we compared the results of the first objective function with a well‐tuned genetic algorithm and differential algorithm, and we also compared the makespan results with one of the popular algorithms for the resource constraints project scheduling problem. Finally, we can observe that the NSGA‐II algorithm presents better solutions than the other two algorithms on average.     

Formally Analyzing Expected Time Complexity of Algorithms Using Theorem Proving

Probabilistic techniques are widely used in the analysis of algorithms to estimate the computational complexity of algorithms or a computational problem. Traditionally, such analyses are performed using paper-and-pencil proofs and the results are sometimes validated using simulation techniques. These techniques are informal and thus may result in an inaccurate analysis. In this paper, we propose a formal technique for analyzing the expected time complexity of algorithms using higher-order-logic theorem proving. The approach calls for mathematically modeling the algorithm along with its inputs, using indicator random variables, in higher-order logic. This model is then used to formally reason about the expected time complexity of the underlying algorithm in a theorem prover. The paper includes the higher-order-logic formalization of indicator random variables, which are fundamental to the proposed infrastructure. In order to illustrate the practical effectiveness and utilization of the proposed infrastructure, the paper also includes the analysis of algorithms for three well-known problems, i.e., the hat-check problem, the birthday paradox and the hiring problem.     

近红外光谱定量模型的优选及在线应用

针对近红外光谱在线监测软件算法模型扩展功能受限和对外服务能力受限的问题,论文设计出一种预测精准且开放服务能力强的近红外在线葡萄糖浓度监测系统。首先,对原始的光谱数据进行预处理,对比多种奇异样本剔除算法以及区间选择算法,从而优选出最佳区间并建立葡萄糖浓度的定量分析模型。然后,将优选模型嵌入到基于java和websocket的后台检测接口中,解决模型嵌入问题,采用微信小程序作为前端展示平台可以很好地解决平台不适应问题。经过测试分析,所嵌入的优选模型具有更好的预测精度,所设计系统可以跨平台多终端远程访问,实时监测效果良好。     

Parallel genetic algorithms: a survey and problem state of the art

In relation with development of computer capabilities and the appearance of multicore processors, parallel computing made it possible to reduce the time for solution of optimization problems. At present of interest are methods of parallel computing for genetic algorithms using the evolutionary model of development in which the main component is the population of species (set of alternative solutions to the problem). In this case, the algorithm efficiency increases due to parallel development of several populations. The survey of basic parallelization strategies and the most interesting models of their implementation are presented. Theoretical ideas on improvement of existing parallelization mechanisms for genetic algorithms are described. A modified model of parallel genetic algorithm is developed. Since genetic algorithms are used for solution of optimization problems, the proposed model was studied for the problem of optimization of a multicriteria function. The algorithm capabilities of getting out of local optima and the influence of algorithm parameters on the deep extremum search dynamics were studied. The conclusion on efficiency of application of dynamic connections of processes, rather than static connections, is made. New mechanisms for implementation and analysis of efficiency of dynamic connections for distributed computing in genetic algorithms are necessary.     

基于多近似模型的交互式遗传算法

人的疲劳向题是交互式遗传算法的核心问题,它制约了交互式遗传算法在复杂优化问题中的应用.为了解决该问题,本文提出基于多近似模型的交互式遗传算法.该算法首先将搜索空间划分,然后利用传统交互式遗传算法得到的数据,在不同子空间生成不同的近似模型,最后采用该模型近似人对进化个体的评价,从而减少人评价的数量,有效解决人的疲劳问题.算法性能分析及在服装进化设计系统中的应用验证了其有效性.     

基于数据库系统的Rough集模型的扩展

针对基于数据库系统的Rough集模型中的知识约简算法对一致决策表适用,而对不一致决策表不适用的局限性,给出了将不一致决策表转换为一致决策表的算法,证明该算法能保持核和约简集合不变,并分析了该算法的时间复杂度,在此基础上,利用数据库系统的集合操作和SQL语言描述转换算法,将基于数据库系统的Rough集模型中的知识约筒算法扩展到不一致决策表．理论分析和实验结果表明,扩展后的算法仍是高效的．     

Incremental K-clique clustering in dynamic social networks

Clustering entities into dense parts is an important issue in social network analysis. Real social networks usually evolve over time and it remains a problem to efficiently cluster dynamic social networks. In this paper, a dynamic social network is modeled as an initial graph with an infinite change stream, called change stream model, which naturally eliminates the parameter setting problem of snapshot graph model. Based on the change stream model, the incremental version of a well known k-clique clustering problem is studied and incremental k-clique clustering algorithms are proposed based on local DFS (depth first search) forest updating technique. It is theoretically proved that the proposed algorithms outperform corresponding static ones and incremental spectral clustering algorithm in terms of time complexity. The practical performances of our algorithms are extensively evaluated and compared with the baseline algorithms on ENRON and DBLP datasets. Experimental results show that incremental k-clique clustering algorithms are much more efficient than corresponding static ones, and have no accumulating errors that incremental spectral clustering algorithm has and can capture the evolving details of the clusters that snapshot graph model based algorithms miss.     

Evolutionary algorithms for optimization problems with uncertainties and hybrid indices

Many optimization problems in real-world applications contain both explicit (quantitative) and implicit (qualitative) indices that usually contain uncertain information. How to effectively incorporate uncertain information in evolutionary algorithms is one of the most important topics in information science. In this paper, we study optimization problems with both interval parameters in explicit indices and interval uncertainties in implicit indices. To incorporate uncertainty in evolutionary algorithms, we construct a mathematical uncertain model of the optimization problem considering the uncertainties of interval objectives; and then we transform the model into a precise one by employing the method of interval analysis; finally, we develop an effective and novel evolutionary optimization algorithm to solve the converted problem by combining traditional genetic algorithms and interactive genetic algorithms. The proposed algorithm consists of clustering of a large population according to the distribution of the individuals and estimation of the implicit indices of an individual based on the similarity among individuals. In our experiments, we apply the proposed algorithm to an interior layout problem, a typical optimization problem with both interval parameters in the explicit index and interval uncertainty in the implicit index. Our experimental results confirm the feasibility and efficiency of the proposed algorithm.