An Optimal Model and Algorithm of Distributed Information System

Distributed information system makes itself be placed in changing file storage position according to the users＇ request pattern. In this paper, we rebuild the model for a management system to turn the process of file managing into a 0-1 programming problem, and present a new individual form to improve the operating efficiency. Aiming at the model, we define a neighborhood span to make segmentation for searching space by using the fitness, based on the region contraction algorithm, present a new evolution algorithm which has the capability of self-adaptively generating new individuals, and ultimately solve the management problem of the distributed file system.     

Reactive Search strategies using Reinforcement Learning,local search algorithms and Variable Neighborhood Search

Optimization techniques known as metaheuristics have been applied successfully to solve different problems, in which their development is characterized by the appropriate selection of parameters (values) for its execution. Where the adjustment of a parameter is required, this parameter will be tested until viable results are obtained. Normally, such adjustments are made by the developer deploying the metaheuristic. The quality of the results of a test instance [The term instance is used to refer to the assignment of values to the input variables of a problem.] will not be transferred to the instances that were not tested yet and its feedback may require a slow process of “trial and error” where the algorithm has to be adjusted for a specific application. Within this context of metaheuristics the Reactive Search emerged defending the integration of machine learning within heuristic searches for solving complex optimization problems. Based in the integration that the Reactive Search proposes between machine learning and metaheuristics, emerged the idea of putting Reinforcement Learning, more specifically the Q-learning algorithm with a reactive behavior, to select which local search is the most appropriate in a given time of a search, to succeed another local search that can not improve the current solution in the VNS metaheuristic. In this work we propose a reactive implementation using Reinforcement Learning for the self-tuning of the implemented algorithm, applied to the Symmetric Travelling Salesman Problem.     

基于邻域搜索的改进最大最小蚁群算法

针对蚁群算法求解旅行商问题时易陷入局部最优的问题,提出一个改进的混合最大最小蚁群算法,并应用于求解旅行商问题.上述算法设计了一种新的信息素更新模型,单个蚂蚁每走一步就进行信息素局部更新,在所有的蚂蚁搜索一周后,最优路径蚂蚁进行全局信息素更新.提出一种新的邻域搜索模型,将邻域大小设置为原来的一半,提高了计算的效率.在每个蚂蚁的一个周期循环后,使用邻域搜索算法优化最优解的路径长度.仿真结果表明,改进算法具有较高的求解精度和收敛速度.     

A hybrid evolutionary algorithm for heterogeneous fleet vehicle routing problems with time windows

This paper presents a hybrid evolutionary algorithm (HEA) to solve heterogeneous fleet vehicle routing problems with time windows. There are two main types of such problems, namely the fleet size and mix vehicle routing problem with time windows (F) and the heterogeneous fixed fleet vehicle routing problem with time windows (H), where the latter, in contrast to the former, assumes a limited availability of vehicles. The main objective is to minimize the fixed vehicle cost and the distribution cost, where the latter can be defined with respect to en-route time (T) or distance (D). The proposed unified algorithm is able to solve the four variants of heterogeneous fleet routing problem, called FT, FD, HT and HD, where the last variant is new. The HEA successfully combines several metaheuristics and offers a number of new advanced efficient procedures tailored to handle the heterogeneous fleet dimension. Extensive computational experiments on benchmark instances have shown that the HEA is highly effective on FT, FD and HT. In particular, out of the 360 instances we obtained 75 new best solutions and matched 102 within reasonable computational times. New benchmark results on HD are also presented.     

核岭回归的邻域保持最大间隔分析的人脸识别

邻域保持嵌入是局部线性嵌入的线性近似,强调保持数据流形的局部结构.改进的最大间隔准则重视数据流形的判别和几何结构,提高了对数据的分类性能.文中提出的核岭回归的邻域保持最大间隔分析既保持流形的局部结构,又使不同类别的数据保持最大间隔,以此构建算法的目标函数.为了解决数据流形高度非线性化的问题,算法采用核岭回归计算特征空间的变换矩阵.先求解数据样本在核子空间中降维映射的结果,再解得核子空间.在标准人脸数据库上的实验表明该算法正确有效,并且识别性能优于普通的流形学习算法.     

Hamiltonian-connected graphs

For a simple graph G, let . In this paper, we prove that if NCD(G)≥|V(G)|, then either G is Hamiltonian-connected, or G belongs to a well-characterized class of graphs. The former results by Dirac, Ore and Faudree et al. are extended.     

基于元胞自动机的股票价格宏观变化的研究

为了研究股票价格在宏观上的变化趋势以及产生这些变化的主要因素.提出了一种新的基于元胞自动机的模型分析股票价格宏观变化,构建了新的股票价格变化规则以及新的股票交易量的变化模型,假设了两大类影响股票价格宏观变化趋势的肉素,同时也细化了股票交易量的函数.模拟了当影响因素都利好,影响因素都利空以及影响因素利好利空交替出现的情况下,股票价格在宏观上的变化情况.仿真结果在一定程度上模拟了股票价格的宏观变化情况和股票价格变化的波动壤集性,说明了假设的两大类因素的合理性以及演化模型的正确性.     

Parallel algorithms for image enhancement and segmentation by region growing, with an experimental study

This paper presents efficient and portable implementations of a powerful image enhancement process, the Symmetric Neighborhood Filter (SNF), and an image segmentation technique that makes use of the SNF and a variant of the conventional connected components algorithm which we call -Connected Components. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient connected components algorithm based on a novel approach for parallel merging. The algorithms have been coded in Split-C and run on a variety of platforms, including the Thinking Machines CM-5, IBM SP-1 and SP-2, Cray Research T3D, Meiko Scientific CS-2, Intel Paragon, and workstation clusters. Our experimental results are consistent with the theoretical analysis (and provide the best known execution times for segmentation, even when compared with machine-specific implementations). Our test data include difficult images from the Landsat Thematic Mapper (TM) satellite data.Also affiliated with the Department of Electrical Engineering.Also affiliated with the Department of Computer Science and the Center for Automation Research.     

Some improved results on communication between information systems

To study the communication between information systems, Wang et al. [C. Wang, C. Wu, D. Chen, Q. Hu, C. Wu, Communicating between information systems, Information Sciences 178 (2008) 3228-3239] proposed two concepts of type-1 and type-2 consistent functions. Some properties of such functions and induced relation mappings have been investigated there. In this paper, we provide an improvement of the aforementioned work by disclosing the symmetric relationship between type-1 and type-2 consistent functions. We present more properties of consistent functions and induced relation mappings and improve upon several deficient assertions in the original work. In particular, we unify and extend type-1 and type-2 consistent functions into the so-called neighborhood-consistent functions. This provides a convenient means for studying the communication between information systems based on various neighborhoods.     

一种有监督的稀疏保持近邻嵌入算法

为充分利用样本的类别信息,提出一种有监督的稀疏保持近邻嵌入算法(SSNPE).该算法结合稀疏表示和保持近邻的思想,根据先验类标签信息保持局部邻域的固有几何关系.采用最小近邻分类器估算识别率,测试结果表明,在姿态、光照和表情变化的情况下,SSNPE都具有较高的识别率.