具有最大总加权满意度的单机调度问题的dynasearch算法

研究了总加权满意程度最大化的单机调度问题.对最优解的性质进行分析和证明,提出该类问题的统治规则.提出该问题新的基于dynasearch邻域的迭代局域搜索算法(ILS).算法主要特点:1)dynasearch是基于多摄动的思想,即一次可以做多个相互独立的交换(或插入);2)用动态规划获得最优dynasearch移动;3)ILS采用随机kick策略对局部最优解进行摄动,然后继续迭代.实现了该问题的两种dynaearch算法;把两种dynasearch算法与统治规则相结合;在进行kick时引入误差限制.实验表明:嵌入统治规则的算法优于没有统治规则的算法;基于dynasearch交换的ILS优于基于dynasearch插入的ILS;dynaearch算法要优于以交换为邻域的多初始点改进算法.

An Improved Binary Search Anti-Collision Protocol for RFID Tag Identification

Radio frequency identification(RFID)has been widespread used in massive items tagged domains.However,tag collision increases both time and energy consumption of RFID network.Tag collision can seriously affect the success of tag identification.An efficient anti-collision protocol is very crucially in RFID system.In this paper,an improved binary search anti-collision protocol namely BRTP is proposed to cope with the tag collision concern,which introduces a Bi-response mechanism.In Bi-response mechanism,two groups of tags allowed to reply to the reader in the same slot.According to Bi-response mechanism,the BRTP strengthens the tag identification of RFID network by reducing the total number of queries and exchanged messages between the reader and tags.Both theoretical analysis and numerical results verify the effectiveness of the proposed BRTP in various performance metrics including the number of total slots,system efficiency,communication complexity and total identification time.The BRTP is suitable to be applied in passive RFID systems.

BHGSO:Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization problem.Since the run time increases exponentially,FS is treated as an NP-hard problem.The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios.This paper presents two binary variants of a Hunger Games Search Optimization(HGSO)algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset.The proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions(BHGSO-V and BHGSO-S).To validate the accuracy,16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms.The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features,classification accuracy,run time,and fitness values than other state-of-the-art algorithms.The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems.The proposed BHGSO-V achieves 95%average classification accuracy for most of the datasets,and run time is less than 5 sec.for low and medium dimensional datasets and less than 10 sec for high dimensional datasets.

Watermark Embedding for Direct Binary Searched Halftone Images by Adopting Visual Cryptography

In this paper,two methods are proposed to embed visual watermark into direct binary search(DBS)halftone images,which are called Adjusted Direct Binary Search(ADBS)and Dual Adjusted Direct Binary Search(DADBS).DADBS is an improved version of ADBS.By using the proposed methods,the visual watermark will be embedded into two halftone images separately,thus,the watermark can be revealed when these two halftone images are overlaid.Experimental results show that both methods can achieve excellent image visual quality and decoded visual patterns.

Binary Gravitational Search based Algorithm for Optimum Siting and Sizing of DG and Shunt Capacitors in Radial Distribution Systems

This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization.

5位高速低功耗Binary-Search模数转换器

基于65 nm CMOS工艺,设计了一种高速低功耗二分搜索算法(Binary-Search)模数转换器(ADC)。与传统Binary-Search结构相比,该ADC的比较器采用两级动态前置放大器和一级动态闩锁器组合构成,减小了静态电流,得到极低的功耗;失调电压降低到不会引起判决误差,省去了外接的数字校准模块。因此,芯片面积减小,避免了校准模块拖慢比较器的工作速度。后仿结果表明,当采样频率为1 GHz时,该Binary-Search ADC的有效位达4.59 bit,功耗仅1.57 mW。

Blocking optimized SIMD tree search on modern processors

Tree search is a widely used fundamental algorithm. Modern processors provide tremendous computing power by integrating multiple cores, each with a vector processing unit. This paper reviews some studies on exploiting single instruction multiple date （SIMD） capacity of processors to improve the performance of tree search, and proposes several improvement methods on reported SIMD tree search algorithms. Based on blocking tree structure, blocking for memory alignment and dynamic blocking prefetch are proposed to optimize the overhead of memory access. Furthermore, as a way of non-linear loop unrolling, the search branch unwinding shows that the number of branches can exceed the data width of SIMD instructions in the SIMD search algorithm. The experiments suggest that blocking optimized SIMD tree search algorithm can achieve 1.6 times response speed faster than the un-optimized algorithm.

A Novel Big Data Storage Reduction Model for Drill Down Search

Multi-level searching is called Drill down search.Right now,no drill down search feature is available in the existing search engines like Google,Yahoo,Bing and Baidu.Drill down search is very much useful for the end user tofind the exact search results among the huge paginated search results.Higher level of drill down search with category based search feature leads to get the most accurate search results but it increases the number and size of thefile system.The purpose of this manuscript is to implement a big data storage reduction binaryfile system model for category based drill down search engine that offers fast multi-levelfiltering capability.The basic methodology of the proposed model stores the search engine data in the binaryfile system model.To verify the effectiveness of the proposedfile system model,5 million unique keyword data are stored into a binaryfile,thereby analysing the proposedfile system with efficiency.Some experimental results are also provided based on real data that show our storage model speed and superiority.Experiments demonstrated that ourfile system expansion ratio is constant and it reduces the disk storage space up to 30%with conventional database/file system and it also increases the search performance for any levels of search.To discuss deeply,the paper starts with the short introduction of drill down search followed by the discussion of important technologies used to implement big data storage reduction system in detail.

An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

Limiting theorems for the nodes in binary search trees

We consider three random variables X_n, Y_n and Z_n, which represent the numbers of the nodes with 0, 1, and 2 children, in the binary search trees of size n. The expectation and variance of the three above random variables are got, and it is also shown that X_n, Y_n and Z_n are all asymptotically normal as n→∞by applying the contraction method.

Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm

Conventional proportional integral derivative(PID)controllers are being used in the industries for control purposes.It is very simple in design and low in cost but it has less capability to minimize the low frequency noises of the systems.Therefore,in this study,a low pass filter has been introduced with the derivative input of the PID controller to minimize the noises and to improve the transient stability of the system.This paper focuses upon the stability improvement of a wind-diesel hybrid power system model(HPSM)using a static synchronous compensator(STATCOM)along with a secondary PID controller with derivative filter(PIDF).Under any load disturbances,the reactive power mismatch occurs in the HPSM that affects the system transient stability.STATCOM with PIDF controller is used to provide reactive power support and to improve stability of the HPSM.The controller parameters are also optimized by using soft computing technique for performance improvement.This paper proposes the effectiveness of symbiosis organisms search algorithm for optimization purpose.Binary coded genetic algorithm and gravitational search algorithm are used for the sake of comparison.

RANDOM BLOCK SEARCH ALGORITHM

Searching is frequently used in computer software design. The research of searching algorithms is always in an important position and many algorithms have been advanced. It is realized that the searching algorithm based on data comparison has the expected complexity of at least O(N log N ) (The complexity means the running time to search N data in a file of N data, the following complexity has the same meaning). Famous computer scientist D. E. Knuth made analysis and comparison for searching algorithms, and came to the conclusion that the binary search has the least complexity of O(N log N). Data processed in comput-

基于ISSA-HKLSSVM的浮选精矿品位预测方法

针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vector Machine,HKLSSVM)的浮选过程精矿品位预测方法.首先采集浮选现场载流X荧光品位分析仪数据作为建模变量并进行预处理,建立基于最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的预测模型,以此构建新型混合核函数,将输入空间映射至高维特征空间,再引入改进麻雀搜索算法对模型参数进行优化,提出基于ISSA-HKLSSVM方法实现精矿品位预测,最后开发基于LabVIEW的浮选精矿品位预测系统对本文提出方法实际验证.实验结果表明,本文提出方法对于浮选过程小样本建模具有良好拟合能力,相比现有方法提高了预测准确率,可实现精矿品位的准确在线预测,为浮选过程的智能调控提供实时可靠的精矿品位反馈信息.

树状结构在数据流求均值中的应用

数据采集过程中,随着数据的增加,数据的平均值持续发生变化。为了研究平均值的变化过程,分析平均值序列的计算方法是有必要的。传统的方法是使用线性顺序存储方式计算均值序列,总的时间复杂度高达O(n2)。提出利用树状结构存储方法求取均值序列,并介绍其实现方法,该方法时间复杂度大大降低,仅为O(n*lnn)。

Xgadget:基于动静结合的二进制Gadget搜索

代码复用技术一直以来都是软件安全研究领域的热点,ROP(return-oriented programming)、JOP(jump-oriented programming)和DOP(data-oriented programming)技术是典型代表。Gadget搜索是代码复用的基础,针对现有静态搜索算法存在支持的Gadget类型不多,不能同时搜索动态链接库等问题,提出了基于动静结合的二进制Gadget搜索方法,基于此开发了搜索工具Xgadget。利用动态映像级插桩,对所有函数进行反汇编;设计了Token级和指令级有穷自动机,实现了基于自动机的静态搜索算法;在五款应用程序中对其进行了测试与评估。实验结果表明,算法支持ROP、JOP、DOP等多种Gadget类型的搜索,能够同时对目标程序动态链接库进行搜索,搜索数量是ROPgadget的12.5倍,单位指令搜索时间较之降低32.1%。算法为不同需求的代码复用提供更为广泛的支持,且输出结果是二进制Gadget,后续复用更直接,有利于代码复用自动化利用。

果蔬采后分级和预冷车辆协同调度模型与算法

新兴的移动式分级、预冷技术应用于果蔬田间采后处理,有助于降低采后损耗,也催生了采后“最先一公里”冷链物流环节协同运作优化问题。以采后分级、预冷环节为例,综合考虑果蔬最佳预冷时间、先分级后预冷的服务顺序等特有协同情景,构建了移动式分级预冷资源协同调度优化模型。与现有模型不同,本研究考虑延迟预冷对果蔬新鲜度的特殊影响,设计了延迟预冷成本函数,在保障产品质量的同时最小化服务运作成本。设计混合遗传算法对模型进行求解,该算法融合了遗传算法与邻域搜索算法,增强混合算法的局部和全局搜索能力。其中,结合问题的双需求特点及关键协同约束,设计了基于双序列的解的表达方式、基于最佳插入策略的交叉算子以及基于三阶段邻域搜索的变异操作,以提高算法的收敛速度与求解质量。通过与标准遗传算法和变邻域搜索算法对比,验证了本文算法在求解大规模算例时可以更快收敛到更高质量的解。基于陕西省洛川县水蜜桃产业的分级预冷数据证明了模型的合理性。本研究有助于把协同运作优化思想引入果蔬采后“最先一公里”冷链物流环节,为降低我国果蔬采后损耗提供创新性解决思路。

基于布谷鸟搜索优化特征选择的轴承故障诊断

针对高维特征导致的滚动轴承故障诊断精度低问题,提出一种基于改进二进制布谷鸟搜索的特征选择方法。首先,采用Hilbert-Huang变换技术提取电机信号的时频域信息,建立高维特征集合。其次,基于布谷鸟搜索算法提出一种基于互信息的特征加权初始化方法,通过计算特征互信息对原始特征集合进行相关性评估,达到快速去除无关特征的目的。同时,引入局部螺旋开发策略,并采用动态切换概率算子实现全局探索和局部开发的平衡,加快算法收敛。最后,采用KNN分类器对轴承状态进行诊断,实验研究表明,上述方法能有效提取更具价值的特征信息,识别精度高达99.05%,相比于其它同类方法诊断准确率高,性能更稳定。

智能反射面协助下的通信感知一体化系统

路径衰弱问题严重制约城市密集区域通信感知一体化系统的性能的提升,为此提出了一种IRS协助下的通信感知一体化系统设计方法。该系统设计的目标是在用户所需SNR、一体化基站发射功率和IRS相位满足系统设计的需求下,通过设计一体化基站的波束成形矢量和IRS的相移矩阵来最大化雷达SNR。然而上述优化问题受IRS相移矩阵高次单位模的限制,要想获取该非凸优化问题的解非常困难,创新性地提出基于MM迭代优化算法,该算法首先利用MM算法对IRS相移矩阵进行降次,然后采用内点法来求解带单位模约束的QCQP问题。为了降低求解IRS相移矩阵子优化问题的复杂度,进一步提出了二分查找算法,来获取相移矩阵的闭式解,使其满足智能驾驶等应用对低时延的需求。实验结果表明相比于没有IRS的传统方法,IRS协助下的通信感知一体化系统可以提供间接的数据通信和目标探测路径,从而有效地提高通信数据传输和雷达目标探测的性能。

中国象棋自动打谱方法研究

针对现存象棋打谱方式繁琐、成本较高的问题,提出了一种基于机器视觉的象棋自动打谱方法。对图像进行预处理后,首先结合二值化与连通区域搜索进行人手遮挡检测,随后采用Hough圆检测、字符矩阵等方法对棋子进行定位,接着将棋子分为红黑两方,并利用局部二进制模式直方图(local binary pattern histogram,LBPH)算法实现棋种识别,最后通过动态识别棋子移动路径,根据行棋规则生成着法。选取50局象棋比赛录像进行测试,结果表明,该方法在识别准确率达到99%的前提下,1 s内可对5帧图像进行处理与识别,且对50个视频识别得到的棋谱正确率均为100%,可以完全满足各类型对局的打谱需求。

基于QUBO模型的信用卡最优获利组合规划

在银行信用卡或相关的贷款业务中,为最大化银行收益、方便银行对客户授信,针对不同信用卡设定不同阈值的情况下进行投资组合,对模拟数据进行归一化处理,建立信用卡最优投资组合二次无约束二值优化(QUBO)模型,使用模拟退火算法求解模型得到单一信用卡与三张信用卡的最大收益阈值组合。进一步通过量子降维方法优化二次无约束二值优化(QUBO)模型,最终得到信用卡选择与阈值的双重最优组合,得出最大收益,辅助提高银行的业务竞争能力。