一种Grover量子搜索算法的改进策略

在使用Grover量子搜索算法对给定规模的数据库搜索时，随着搜索目标数的增加，获得正确结果的概率大幅度下降．分析了出现这种现象的原因，提出了一种基于新的相位匹配条件的改进策略．在新的相位匹配条件中，使2次相位旋转的大小相等方向相反．当要搜索的目标数目多于记录总数的1／3时，应用改进后的算法只需一步搜索，能以至少25／27的概率得到全部搜索目标．实验证明这种策略是有效的．     

A quantum algorithm using NMR computers to break secret-key cryptosystems

In this paper, we discuss quantum algorithms that, for a given plaintextm _o and a given ciphertextc _o, will find a secret key,k _o, satisfyingc _o=E(k _o,m _o), where an encryption algorithm,E, is publicly available. We propose a new algorithm suitable for an NMR (Nuclear Magnetic Resonance) computer based on the technique used to solve the counting problem. The complexity of, our algorithm decreases as the measurement accuracy of the NMR computer increases. We discuss the possibility that the proposed algorithm is superior to Grover’s algorithm based on initial experimental results. Kazuo Ohta, Dr.S.: He is Professor of Faculty of Electro-Communications at the University of Electro-Communications, Japan. He received B.S., M.S., and Dr. S. degrees from Waseda University, Japan, in 1977, 1979, and 1990, respectively. He was researcher of NTT (Nippon Telegraph and Telephone Corporation) from 1979 to 2001, and was visiting scientist of Laboratory for Computer Science e of MIT (Massachusetts Institute of Technology) in 1991–1992 and visiting Professor of Applied Mathematics of MIT in 2000. He is presently engaged in research on Information Security, and theoretical computer science. Dr. Ohta is a member of IEEE, the International Association for Cryptologic Research, the Institute of Electronics, Information and Communication Engineers and the Information Processing Society of Japan. Tetsuro Nishino,: He received the B.S., M.S. and, D.Sc. degrees in mathematics from Waseda University, in 1982, 1984, and 1991 respectively. From 1984 to 1987, he joined Tokyo Research Laboratory, IBM Japan. From 1987 to 1992, he was a Research Associate of Tokyo Denki University, and from 1992 to 1994, he was an Associate Professor of Japan Advanced Institute of Science and Technology, Hokuriku. He is presently an Associate Professor in the Department of Communications and Systems Engineering, the University of Electro-Communications. His main interests are circuit complexity theory, computational learning theory and quantum complexity theory. Seiya Okubo,: He received the B.Eng. and M.Eng. degrees from the University of Electro-Communications in 2000 and 2002, respectively. He is a student in Graduate School of Electro-Communications, the University of Electro-Communications. His research interests include quantum complexity theory and cryptography. Noboru Kunihiro, Ph.D.: He is Assistant Professor of the University of Electro-Communications. He received his B. E., M. E. and Ph. D. in mathematical engineering and information physics from the University of Tokyo in 1994, 1996 and 2001, respectively. He had been engaged in the research on cryptography and information security at NTT Communication Science Laboratories from 1996 to 2002. Since 2002, he has been working for Department of Information and Communication Engineering of the University of Elector-Communications. His research interests include cryptography, information security and quantum computations. He was awarded the SCIS’97 paper prize.     

改进的多目标元素量子搜索算法

Grover量子搜索算法解决了未加排序的数据库搜索问题,在2ⁿ个元素中搜索M个目标元素,其计算复杂度为O（（2ⁿ/M）^-2）,相对于经典算法实现了二次加速,但是,当目标元素个数接近2ⁿ/2时该算法成功率只达到50%。从任意相位的Grover变换从发,给出一种改进的多目标元素量子搜索算法,该算法在目标元素个数M≥2ⁿ/4时,只用一次Grover变换就能以概率1完成搜索。     

A recursive search algorithm for statistical disclosure assessment

A new algorithm, SUDA2, is presented which finds minimally unique itemsets i.e., minimal itemsets of frequency one. These itemsets, referred to as Minimal Sample Uniques (MSUs), are important for statistical agencies who wish to estimate the risk of disclosure of their datasets. SUDA2 is a recursive algorithm which uses new observations about the properties of MSUs to prune and traverse the search space. Experimental comparisons with previous work demonstrate that SUDA2 is several orders of magnitude faster, enabling datasets of significantly more columns to be addressed. The ability of SUDA2 to identify the boundaries of the search space for MSUs is clearly demonstrated.     

OCOG: A common grasp computation algorithm for a set of planar objects

This paper addresses the problem of defining a simple End-Effector design for a robotic arm that is able to grasp a given set of planar objects. The OCOG (Objects COmmon Grasp search) algorithm proposed in this paper searches for a common grasp over the set of objects mapping all possible grasps for each object that satisfy force closure and quality criteria by taking into account the external wrenches (forces and torque) applied to the object. The mapped grasps are represented by feature vectors in a high-dimensional space. This feature vector describes the design of the gripper. A database is generated for all possible grasps for each object in the feature vector space. A search algorithm is then used for intersecting all possible grasps over all parts and finding a common grasp suitable for all objects. The search algorithm utilizes the kd-tree index structure for representing the database of the sets of feature vectors. The kd-tree structure enables an efficient and low cost nearest-neighbor search for common vectors between the sets. Each common vector found (feature vector) is the grasp configuration for a group of objects, which implies the future end-effector design. The final step classifies the grasps found to subsets of the objects, according to the common vectors found. Simulations and experiments are presented for four objects to validate the feasibility of the proposed algorithm. The algorithm will be useful for standardization of end-effector design and reducing its engineering time.     

基于量子遗传混合算法的泊位联合调度

为了提高集装箱港口服务效率,减少船舶服务的拖期费用,针对港口硬件（泊位、拖轮、岸桥）既定条件下的拖轮-泊位联合调度问题,新建了以最小化总体船舶在港时间和总拖期时间为目标的数学模型,设计了一种混合算法进行求解。首先,分析确定了将量子遗传算法（QGA）和禁忌搜索（TS）算法进行串行混合的策略;然后,依据该联合调度问题特点,在解决算法实施中的关键技术问题（染色体结构设计和测量、遗传操作、种群更新等）的同时,采用了动态量子旋转门更新机制;最后,用生产实例验证了算法的可行性及有效性。算法实验结果表明,与人工调度结果相比,混合算法的总体船舶在港时间和总拖期时间分别减少了24%和42.7%;与遗传算法结果相比,分别减少了10.9%和22.5%。所提模型及算法不仅能为港口船舶的入泊、离泊和装卸作业环节提供优化作业方案,而且能增强港口竞争力。     

改进量子遗传算法用于多峰值函数优化

传统遗传算法（SGA）在处理多峰值函数优化问题中存在局部收敛性的问题,最初的量子遗传算法（QGA）也存在这一问题。运用一种改进量子遗传算法（MQGA）,有效地解决了一些多峰值函数的优化问题。根据几个重要的测试函数进行仿真实验结果证明,与SGA和QGA相比,改进的量子遗传算法（MQGA）在一些多峰值优化问题中更具有效性和可行性。     

A new algorithm for clustering search results

We develop a new algorithm for clustering search results. Differently from many other clustering systems that have been recently proposed as a post-processing step for Web search engines, our system is not based on phrase analysis inside snippets, but instead uses latent semantic indexing on the whole document content. A main contribution of the paper is a novel strategy – called dynamic SVD clustering – to discover the optimal number of singular values to be used for clustering purposes. Moreover, the algorithm is such that the SVD computation step has in practice good performance, which makes it feasible to perform clustering when term vectors are available. We show that the algorithm has very good classification performance, and that it can be effectively used to cluster results of a search engine to make them easier to browse by users. The algorithm has being integrated into the Noodles search engine, a tool for searching and clustering Web and desktop documents.     

A new polynomial algorithm for a parallel identical scheduling problem

A precedence order is defined based on the release dates of jobs' direct successors. Using the defined precedence order and Heap Sort, a new polynomial algorithm is provided which aims to solve the parallel scheduling problem P|p = 1, r ,outtree|∑C . The new algorithm is shown to be more compact and easier to implement.     

A new polynomial algorithm for a paralle identical scheduling problem

A precedence order is defined based on the release dates of jobs' direct successors. Using the defined precedence order and Heap Sort, a new polynomial algorithm is provided which aims to solve the parallel scheduling problem P|p = 1, r ,outtree|∑C . The new algorithm is shown to be more compact and easier to implement.     

求解旅行商问题的改进型量子蚁群算法

针对传统量子蚁群算法在求解TSP时容易陷入局部最优以及收敛速度较慢,提出了一种求解旅行商问题的改进型量子蚁群算法（IQACA）。该算法设计了一种新信息素挥发因子的自适应动态更新策略,对信息素进行动态更新;并采用一种新的量子旋转门对量子概率幅值的收敛趋势进行改变。通过三个基本函数极值优化仿真与传统量子蚁群算法进行对比,证明算法性能较优。基于TSPLIB的仿真实验与其他几种算法进行比较,结果表明,算法具有较快的收敛速度,提高了解的全局性,有效避免了算法陷入局部最优。     

An <Emphasis Type="Italic">R</Emphasis> || <Emphasis Type="Italic">C</Emphasis><Subscript>max</Subscript> Quantum Scheduling Algorithm

Grover’s search algorithm can be applied to a wide range of problems; even problems not generally regarded as searching problems, can be reformulated to take advantage of quantum parallelism and entanglement, and lead to algorithms which show a square root speedup over their classical counterparts. In this paper, we discuss a systematic way to formulate such problems and give as an example a quantum scheduling algorithm for an R||C_max problem. R||C_max is representative for a class of scheduling problems whose goal is to find a schedule with the shortest completion time in an unrelated parallel machine environment. Given a deadline, or a range of deadlines, the algorithm presented in this paper allows us to determine if a solution to an R||C_max problem with N jobs and M machines exists, and if so, it provides the schedule. The time complexity of the quantum scheduling algorithm is while the complexity of its classical counterpart is .     

基于改进量子进化算法的特征选择

特征选择作为一种数据预处理技术被广泛研究,由于其具有NP难度而一直无法找到有效的求解方法。鉴于目前在特征选择中应用较多的遗传算法存在进化机制上的局限,将量子进化算法应用于特征选择,提出了一种基于改进量子进化算法的特征选择算法。以增加种群多样性和提高寻优性能为目标改进了量子进化算法,以Fisher比和特征维度为特征子集的评价准则构造了适应度函数,按照量子进化算法求解优化问题的步骤设计了特征选择算法。使用UCI数据库中的数据集对三种算法作对比验证,通过识别重要特征、提高学习算法性能、特征选择效率三组实验,结果表明,该算法能够识别出重要特征,并随着数据集特征维度升高,特征选择的性能逐渐优于对比算法,到了高维数据集,特征选择效率明显优于对比算法。     

A new computational algorithm for retrial queues to cellular mobile systems with guard channels

Retrial queueing models have been applied to evaluate the impact of new and handover calls on the call control mechanisms of cellular mobile networks. The fact that the retrial rate depends on the number of retried calls waiting in the system leads to an analytically intractable model. Therefore, approximation procedures should be used to compute the performability of the system. However, there exists a numerical problem concerning a recursive algorithm to compute the stationary probabilities for retrial queues modeling guard channels. Namely, the consideration of guard channels leads to calculations with negative terms in the recursive algorithm. Negative terms and extremely small values involved in the computation are the main causes for a numerical instability in the recursive algorithm.     

求解作业车间调度问题的差分和声搜索算法

为能够应用和声搜索算法(HSA)高效求解作业车间调度问题(JSSP),提出一种新的差分和声搜索算法(DEHSA)。首先,针对和声函数连续而工序离散现象,设计了排序工序数量转换法,将浮点数的和声转换成工件序列;其次,为提高HSA的收敛速度,改进了HSA的进化模式,不仅是替换一个最差解,还提出了和声变量进化时依赖于当前最优解的“导优”概率;最后,将差分进化算法(DEA)引入到HSA中,克服了HSA方向性差和后期停滞的现象。仿真实验结果表明,DEHSA在求解JSSP上具有可行性和有效性。     

具有捕食策略的混合随机优化算法及其多极值函数优化

SAGACIA是一种混合随机优化算法,该算法虽已吸收了模拟退火算法、遗传算法和趋化性算法的优点,但搜索过程中仍存在收敛速度慢以及采用固定步长影响搜索精度的缺点,而捕食搜索策略通过限制的调节能较快锁定最优区域,从而提高收敛速度。结合两者的优缺点,提出一种具有捕食搜索策略的自适应调整步长SAGACIA算法,改进后的算法通过捕食搜索策略平衡了算法的局域搜索和全局搜索,提高了收敛速度;邻域搜索采用自适应步长,避免了最优解附近的震荡,提高了搜索精度。实验仿真结果表明,改进后的SAGACIA算法具有较快的收敛速度和较高的寻优精度,证明了算法改进的有效性和可行性。     

数字滤波器设计的文化量子算法

有限脉冲响应(FIR)和无限脉冲响应(IIR)数字滤波器的设计实质可看作是多参数优化问题。为实现高效的数字滤波器,首先将滤波器的设计转化为滤波器参数的约束优化问题,然后提出文化量子(CQ)算法在参数空间进行并行搜索以获得滤波器设计的最优参数值。提出的文化量子算法结合文化原理,在量子种群空间更新中使用了量子旋转门的知识进化机制,是一种可用于实数解优化的快速多维搜索算法。计算机仿真实验表明在对FIR和IIR数字滤波器设计时,文化量子算法的收敛速度和性能都优于粒子群,量子粒子群以及自适应量子粒子群优化等算法,证明了该方法的有效性和优越性。     

一种新的相位角编码量子进化算法

针对具有连续解空间的数值函数优化问题,基于量子算法和实数编码进化算法的思想,提出一种新的相位角编码量子进化算法(PAQEA).算法的概率表达特性使得量子染色体能够以一定概率表达优化问题的所有可行解,结合动态量子旋转门实现染色体的进化,实现了算法局部搜索与全局搜索的平衡.理论分析证明了算法的全局收敛性.仿真结果表明,该算法适用于复杂数值函数优化问题,具有收敛速度快、搜索能力强和稳定性高的特点.     

A Hybrid Framework for Over-Constrained Generalized

In this work we study an over-constrained scheduling problem where constraints cannot be relaxed. This problem originates from a local defense agency where activities to be scheduled are strongly ranked in a priority scheme determined by planners ahead of time and operational real-time demands require solutions to be available almost immediately. A hybrid framework is used which is composed of two levels. A high-level component explores different orderings of activities by priorities using Tabu Search or Genetic Algorithm heuristics, while in a low-level component, constraint programming and minimal critical sets are used to resolve conflicts. Real-data used to test the algorithm show that a larger number of high priority activities are scheduled when compared to a CP-based system used currently. Further tests were performed using randomly generated data and results compared with CPLEX. The approach provided in this paper offers a framework for problems where all constraints are treated as hard constraints and where conflict resolution is achieved only through the removal of variables rather than constraints.     

求解作业车间调度问题的混合帝国主义竞争算法

针对最小化最大完工时间的作业车间调度问题（JSP）,提出一种结合帝国主义竞争算法（ICA）和禁忌搜索（TS）算法的混合算法。混合算法以帝国主义竞争算法为基础,在同化操作中融入遗传算法中的杂交算子和变异算子,使算法全局搜索能力更强。为了克服帝国主义竞争算法局部搜索能力弱的缺点,引入禁忌搜索算法进一步优化同化操作后的后代。禁忌搜索算法采用混合邻域结构和新型选择策略,使得算法能够更有效地搜索邻域解。混合算法兼具全局搜索能力和局部搜索能力,通过对13个经典的Benchmark调度问题进行仿真测试,并与近年4种新型混合算法进行对比分析,实验结果表明了所提算法求解Job Shop调度问题的有效性和稳定性。