一种多目标置换流水车间调度问题的优化算法

针对最大完工时间最小和总流经时间最小的多目标置换流水车间调度问题(permutation flow shopscheduling problem, PFSP), 提出一种粒子群优化算法与变邻域搜索算法结合的混合粒子群优化(hybrid particleswarm optimization algorithm, HPSO)算法, 并使算法在集中搜索和分散搜索之间达到合理的平衡. 在该混合算法中,采用NEH 启发式算法进行种群初始化, 以提高初始解质量;运用随机键表示法设计基于升序排列规则(ranked-order-value, ROV), 将连续PSO 算法应用于置换流水车间调度问题;引入外部档案集存贮Pareto 解, 并采用强支配关系和聚集距离相结合的混合策略保证解集的分布性;采用Sigma 法和基于聚集距离的轮盘赌法进行全局最优解的选择;提出变邻域搜索算法, 对外部集中的Pareto 解作进一步地局部搜索. 最后, 运用提出的混合算法求解Taillard 基准测试集, 并将测试结果与SPEA2 算法进行比较, 验证该调度算法的有效性.     

RFID二进制树型折半搜索防碰撞算法

为提高RFID系统中多标签读取的工作效率,分析了二进制树型搜索防碰撞算法,提出一种改进型的二进制树型折半搜索算法．通过构建哈夫曼树,使用自定义编码,采用堆栈操作确定碰撞位,用折半查找方式减少重复路径访问量,并最终确定最短路径长度．数据分析及实验结果表明,二进制树型折半搜索防碰撞算法可以明显降低搜索深度,显著提高RFID多标签读取的工作效率。     

HiggsBounds 2.0.0: Confronting neutral and charged Higgs sector predictions with exclusion bounds from LEP and the Tevatron

HiggsBounds 2.0.0 is a computer code which tests both neutral and charged Higgs sectors of arbitrary models against the current exclusion bounds from the Higgs searches at LEP and the Tevatron. As input, it requires a selection of model predictions, such as Higgs masses, branching ratios, effective couplings and total decay widths. HiggsBounds 2.0.0 then uses the expected and observed topological cross section limits from the Higgs searches to determine whether a given parameter scenario of a model is excluded at the 95% C.L. by those searches. Version 2.0.0 represents a significant extension of the code since its first release (1.0.0). It includes now 28/53 LEP/Tevatron Higgs search analyses, compared to the 11/22 in the first release, of which many of the ones from the Tevatron are replaced by updates. As a major extension, the code allows now the predictions for (singly) charged Higgs bosons to be confronted with LEP and Tevatron searches. Furthermore, the newly included analyses contain LEP searches for neutral Higgs bosons (H) decaying invisibly or into (non-flavour tagged) hadrons as well as decay-mode independent searches for neutral Higgs bosons, LEP searches via the production modes τ⁺τ⁻H and , and Tevatron searches via . Also, all Tevatron results presented at the ICHEP?10 are included in version 2.0.0. As physics applications of HiggsBounds 2.0.0 we study the allowed Higgs mass range for model scenarios with invisible Higgs decays and we obtain exclusion results for the scalar sector of the Randall–Sundrum model using up-to-date LEP and Tevatron direct search results.

Program summary

Program title: HiggsBoundsCatalogue identifier: AEFF_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEFF_v2_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: GNU General Public Licence version 3No. of lines in distributed program, including test data, etc.: 74 005No. of bytes in distributed program, including test data, etc.: 1 730 996Distribution format: tar.gzProgramming language: Fortran 77, Fortran 90 (two code versions are offered).Classification: 11.1.Catalogue identifier of previous version: AEFF_v1_0Journal reference of previous version: Comput. Phys. Comm. 181 (2010) 138External routines: HiggsBounds requires no external routines/libraries. Some sample programs in the distribution require the programs FeynHiggs 2.7.1 or CPsuperH2.2 to be installed.Does the new version supersede the previous version?: YesNature of problem: Determine whether a parameter point of a given model is excluded or allowed by LEP and Tevatron neutral and charged Higgs boson search results.Solution method: The most sensitive channel from LEP and Tevatron searches is determined and subsequently applied to test this parameter point. The test requires as input, model predictions for the Higgs boson masses, branching ratios and ratios of production cross sections with respect to reference values.Reasons for new version: This version extends the functionality of the previous version.Summary of revisions: List of included Higgs searches has been expanded, e.g. inclusion of (singly) charged Higgs boson searches. The input required from the user has been extended accordingly.Restrictions: Assumes that the narrow width approximation is applicable in the model under consideration and that the model does not predict a significant change to the signature of the background processes or the kinematical distributions of the signal cross sections.Running time: About 0.01 seconds (or less) for one parameter point using one processor of an Intel Core 2 Quad Q6600 CPU at 2.40 GHz for sample model scenarios with three Higgs bosons. It depends on the complexity of the Higgs sector (e.g. the number of Higgs bosons and the number of open decay channels) and on the code version.     

Interactive multiobjective fuzzy random programming through the level set-based probability model

This paper considers a multiobjective linear programming problem involving fuzzy random variable coefficients. A new fuzzy random programming model is proposed by extending the ideas of level set-based optimality and a stochastic programming model. The original problem involving fuzzy random variables is transformed into a deterministic equivalent problem through the proposed model. An interactive algorithm is provided to obtain a satisficing solution for a decision maker from among a set of newly defined Pareto optimal solutions. It is shown that an optimal solution of the problem to be solved iteratively in the interactive algorithm is analytically obtained by a combination of the bisection method and the simplex method.     

A mixture of generalized latent variable models for mixed mode and heterogeneous data

In the behavioral, biomedical, and social-psychological sciences, mixed data types such as continuous, ordinal, count, and nominal are common. Subpopulations also often exist and contribute to heterogeneity in the data. In this paper, we propose a mixture of generalized latent variable models (GLVMs) to handle mixed types of heterogeneous data. Different link functions are specified to model data of multiple types. A Bayesian approach, together with the Markov chain Monte Carlo (MCMC) method, is used to conduct the analysis. A modified DIC is used for model selection of mixture components in the GLVMs. A simulation study shows that our proposed methodology performs satisfactorily. An application of mixture GLVM to a data set from the National Longitudinal Surveys of Youth (NLSY) is presented.     

The analysis of Range Quickselect and related problems

Range Quickselect, a simple modification of the well-known Quickselect algorithm for selection, can be used to efficiently find an element with rank k in a given range [i..j], out of n given elements. We study basic cost measures of Range Quickselect by computing exact and asymptotic results for the expected number of passes, comparisons and data moves during the execution of this algorithm.The key element appearing in the analysis of Range Quickselect is a trivariate recurrence that we solve in full generality. The general solution of the recurrence proves to be very useful, as it allows us to tackle several related problems, besides the analysis that originally motivated us.In particular, we have been able to carry out a precise analysis of the expected number of moves of the pth element when selecting the jth smallest element with standard Quickselect, where we are able to give both exact and asymptotic results.Moreover, we can apply our general results to obtain exact and asymptotic results for several parameters in binary search trees, namely the expected number of common ancestors of the nodes with rank i and j, the expected size of the subtree rooted at the least common ancestor of the nodes with rank i and j, and the expected distance between the nodes of ranks i and j.     

IMS网络中的SIP洪泛攻击检测

为了检测针对3G核心网中IP多媒体子系统的SIP(session initiation protocol)洪泛攻击,提出了一种双抽样多点检测方法.该方法在使用计数式布鲁姆过滤器统计检测特征信息的基础上,将检测空间划分为5个范围,即正常范围、关注范围、检测范围、精检测范围和攻击范围,然后对落在不同范围内的统计信息给予相应...     

利用多级局部性实现可扩展的无结构P2P 搜索

无结构P2P网络拓扑随着规模的增大会出现一定的统计特性,充分应用该现象提出了一种多级局部覆盖网络(multi-level local overlay,简称ML2O)的无结构P2P覆盖网,对ML2O中节点间的连接进行恰当的数学控制后,就能使产生的拓扑具有从微观到宏观的多个粒度上的局部性.理论分析表明,ML2O的网络直径和节点平均度都是网络规模n的对数,为其上建立可扩展的无结构P2P搜索奠定了基础.给出了应用ML2O多粒度局部特性的索引机制:首先以局部为单位建立信息索引;然后在局部索引的基础上建立更大粒度局部的索引,从而形成一棵索引树;最后提出了一种局部渗透定向搜索算法(local pervasion and directed search,简称LPDS).LPDS用局部渗透收集到的信息建立部分索引树,并在树上找到更接近搜索目标的下一跳.理论分析表明,LPDS搜索算法的平均搜索跳数和通信负载都是O(logn).模拟实验结果表明,ML2O上LPDS的可扩展性接近结构化P2P搜索,其鲁棒性接近无结构P2P搜索.