基于证据理论决策的蚁群优化算法

针对基本蚁群算法中存在的早熟现象,提出了基于证据理论的搜索方法.该方法在每个蚂蚁遍历到某节点时就进行信息融合,并将融合结果动态更新该节点的信息素,使得在蚂蚁个体寻优过程中隔代强化了,并在随机搜索过程中呈现自组织特性,蚂蚁个体利用各自的遍历信息不断加强优秀可行解的权重,从而有效地降低了搜索空间,提高了搜索效率.仿真结果显示,该方法在有无先验知识的情况下,都能在解空间的全局寻优时具有良好的收敛性和鲁棒性.     

Fuzzy Distance Transform: Theory, Algorithms, and Applications

This paper describes the theory and algorithms of distance transform for fuzzy subsets, called fuzzy distance transform (FDT). The notion of fuzzy distance is formulated by first defining the length of a path on a fuzzy subset and then finding the infimum of the lengths of all paths between two points. The length of a path π in a fuzzy subset of the n-dimensional continuous space ⁿ is defined as the integral of fuzzy membership values along π. Generally, there are infinitely many paths between any two points in a fuzzy subset and it is shown that the shortest one may not exist. The fuzzy distance between two points is defined as the infimum of the lengths of all paths between them. It is demonstrated that, unlike in hard convex sets, the shortest path (when it exists) between two points in a fuzzy convex subset is not necessarily a straight line segment. For any positive number θ≤1, the θ-support of a fuzzy subset is the set of all points in ⁿ with membership values greater than or equal to θ. It is shown that, for any fuzzy subset, for any nonzero θ≤1, fuzzy distance is a metric for the interior of its θ-support. It is also shown that, for any smooth fuzzy subset, fuzzy distance is a metric for the interior of its 0-support (referred to as support). FDT is defined as a process on a fuzzy subset that assigns to a point its fuzzy distance from the complement of the support. The theoretical framework of FDT in continuous space is extended to digital cubic spaces and it is shown that for any fuzzy digital object, fuzzy distance is a metric for the support of the object. A dynamic programming-based algorithm is presented for computing FDT of a fuzzy digital object. It is shown that the algorithm terminates in a finite number of steps and when it does so, it correctly computes FDT. Several potential applications of fuzzy distance transform in medical imaging are presented. Among these are the quantification of blood vessels and trabecular bone thickness in the regime of limited special resolution where these objects become fuzzy.     

蚁群优化算法应用研究概述

为了对蚁群算法近年来的应用研究情况进行总结,为后续的研究提供参考,综述了蚁群算法近年来的应用研究成果.在介绍蚁群算法基本特点的基础上,首先介绍了蚁群算法的一般模型,其次概述了蚊群算法近年来的研究进展与算法在各个领域的应用情况,最后指出了仍未解决的问题和当前的研究热点.蚁群系统所具有的分布式组织模型对解决复杂组合优化问题、分布控制问题提供了很好的思路,对蚁群系统行为及其白组织能力的研究具有积极意义.     

蚁群聚类算法综述

数据聚类是重要的数据挖掘技术,在工程和技术等领域具有广泛的应用背景。蚁群算法作为一种新型的优化方法,具有很强的鲁棒性和适应性。文章着重介绍蚁群聚类算法的研究情况,阐述当今流行的蚁群聚类算法的基本原理及其特性,旨在为蚁群聚类算法的发展提供引导作用。     

When Ants Attack: Ant Algorithms for Constraint Satisfaction Problems

We describe an ant algorithm for solving constraint problems (Solnon 2002, IEEE Transactions on Evolutionary Computation 6(4): 347–357). We devise a number of variants and carry out experiments. Our preliminary results suggest that the best way to deposit pheromone and the best heuristics for state transitions may differ from current practice     

求解QAP问题的近似骨架导向快速蚁群算法

QAP(quadratic assignment problem)问题是经典的组合优化问题之一,广泛应用于许多领域中.针对QAP问题,提出了一种新的蚁群算法--近似骨架导向的快速蚁群算法(ABFANT).该算法的基本原理是通过对局部最优解的简单相交操作得到QAP问题实例的近似骨架(approximate-backbone),利用这些近似骨架可以极大地缩小QAP问题的搜索空间,而同时不降低搜索的性能,最后对这个缩小后的搜索空间,直接用当前求解QAP问题最好的启发式算法之一-快速蚁群算法(FANT)求解得到问题的解.在QAPLIB中的典型实例上的实验结果表明,近似骨架导向的快速蚁群算法明显优于快速蚁群算法.此外,指出基于近似骨架的算法思想可以很容易地被移植到其他求解QAP问题的启发式算法中.     

基于蚂蚁算法的关联规则挖掘

蚁群算法是一种新型的用于求解组合优化或函数优化问题的启发式算法,其基本思想是借用生物界的蚂蚁群体觅食机理,将每个蚂蚁看作一个智能体.本文提出一种基于蚁群系统的数据挖掘中的关联规则挖掘模式。这种基于启发式优化算法的模式可以为用户决策提供一个很好的支持。这种方式可以很快的找到关联规则也可以确保规则结果的准确性。最后用实验结果的对比来进一步说明这种方法的高效和准确性。     

基于蚁群算法的文字识别

在智能自动化领域,具有群体智能特征的蚁群算法引起了人们的广泛关注。目前,在文字识别方面的研究仍较少,而在当今社会很多方面都需要用到文字图像边缘的检测与文字识别,在文字信息泛滥的时代,利用人工识别技术检测识别图片、纸张、广告牌、视频等媒介上的文字已经变得不太现实。为了准确、高效地检测识别出文字,在学习了蚁群算法的基本原理之后,改进了传统的蚁群算法,并将其应用于图像分割、提取文字领域,同时结合图像文字识别技术OCR和纹理特征检测文字图像边缘,由此提高文字检测识别的准确性。     

蚁群算法及其改进形式综述

蚁群算法是一种具有许多优良特性的模拟进化算法,已经成功地解决了许多复杂的组合优化问题。但是蚁群算法并不完善。本文介绍蚁群算法的模型及其存在的问题,并综述蚁群算法的多种改进形式,最后对蚁群算法将来的研究方向作出预测。     

图的树分解及其算法应用研究进展

图的树宽和树分解是图子式理论中发展起来的两个重要概念。图的树分解由于其本身的特性使得它在算法设计中有着极其重要的意义。从图的树宽特性、图的树分解算法、图的树分解在复杂算法问题求解中的应用等方面对近年来的相关研究进展做了深入的分析和介绍,结合一些简洁的实例分析了一些重要的原理和方法,讨论了其中的一些问题,并给出了今后的一些研究方向。     

蚁群优化算法的收敛性分析

有关蚁群优化算法收敛性分析的研究还很少．不利于进一步改进其算法．为此,较详细地分析了用蚁群优化算法求解TSP问题的收敛性．证明了当0〈q0〈1时．算法能够收敛到最优解．分析了封闭路径性质、启发函数、信息素和q0对收敛性的影响．据此给出了提高算法收敛速度的几点结论．     

蚁群算法的研究及应用进展

蚁群算法是优化领域新出现的一种启发式仿生类并行智能进化系统,该算法采用分布式计算和正反馈机制,易于和其他算法结合,目前已得到了广泛的应用。本文在介绍基本蚁群算法的基础上,介绍了蚁群算法目前的一些研究情况,然后例举了蚁群算法的一些应用,最后对蚁群算法今后的研究方向作了分析和展望。     

Algebraic Algorithmics: Theory and Applications

This article is a review of works on the algebra of algorithms, which is a new promising line of investigation in the field of algebraic algorithmics and is progressing rapidly in Ukraine and abroad. To this line belong explorations into a multilevel structural software design method developed at the Automatic Programming Department at the V. M. Glushkov Cybernetics Institute of the Academy of Sciences of Ukraine.     

代数次数的求解算法及其在SIMON-like算法中的应用

代数次数作为布尔函数重要的密码学指标,在密码算法的设计与分析中有着重要的应用.主要研究布尔函数代数次数的求解及其在分组密码SIMON-like算法中的应用.首先,在利用真值表求解代数正规型算法的基础上建立了基于CUDA的并行求解架构,协同利用CPU和GPU的计算资源,极大地缩短了求解代数次数的时间,在较短的时间内求解了SIMON32算法和SIMECK32算法任意轮数的代数正规型和代数次数;其次,在Cube攻击理论的基础上,根据代数次数和超多项式取值之间的关系,设计了估计代数次数的概率算法,估计了一般SIMON-like算法布尔函数的代数次数;最后,从布尔函数代数次数的角度出发,给出了SIMON-like算法在选择不同循环移位参数表现的差异性,进而给出循环移位参数的选取依据.实验结果表明,SIMON算法在原始参数下,达到最大代数次数所需的轮数最短,原始参数具有更高的安全性.     

Bandelets理论及其离散算法

介绍了一类新的变换基bandelet,它在多尺度图像分解的基础上,自适应地提取图像的几何流.几何流表明了图像灰色值变化的正则性.同时描述了第二代离散bandelet基的构建以及它的应用算法.Bandelet变换具有正交性,其对应的基函数是正则的.对几何正则图像,它的几何流是最优的,通过bandelet变换可产生最优渐进衰减误差,所以在图像的压缩和除噪等方面,具有广泛的应用前景.通过试验对bandelet变换和小波变换进行了比较.     

Partitioning Planar Graphs with Vertex Costs: Algorithms and Applications

We prove separator theorems in which the size of the separator is minimized with respect to non-negative vertex costs. We show that for any planar graph G there exists a vertex separator of total sum of vertex costs at most and that this bound is optimal to within a constant factor. Moreover, such a separator can be found in linear time. This theorem implies a variety of other separation results. We describe applications of our separator theorems to graph embedding problems, to graph pebbling, and to multicommodity flow problems. Received June 1997; revised February 1999.     

Simulating Network Delays: Applications,Algorithms, and Tools

Organizations are rushing to establish a presence on the World Wide Web (WWW). Researchers, designers, and users all recognize the importance of network delays, with longer delays frequently being associated with more negative user experiences. Although some delay is unavoidable, design decisions do contribute to the total delay users experience. In this article, a collection of tools that allow individuals to experience realistic network delays during informal evaluations, usability studies, and controlled experiments are described. These tools allow practitioners to more effectively assess usability in the context of realistic network delays, researchers to more effectively investigate the factors that affect the usability of information and applications delivered via the WWW, and educators to more effectively convey the importance of design decisions in the context of the WWW. This article describes how these tools may be used as well as the tools themselves, including the algorithms that make them effective. 2 approaches for validating simulations with results are presented. The first validation suggests that the simulation process utilized in the wide-area network delay simulator tools (Borella & Sears, 1997) effectively reproduces the network delays observed when retrieving material via the WWW. The second validation provides even stronger support, indicating that the simulation process can be used to reproduce a specific set of network conditions more accurately than the network itself. Directions for additional research are also discussed.     

数据挖掘:建模、算法、应用和系统

数据挖掘是20世纪末逐渐形成的一个多学科交叉领域,目前已经广泛成功地应用在金融、零售、医药、通讯、电子工程、航空、旅馆等有大量数据和深度分析需求的领域。文中对数据挖掘的建模、算法、应用和软件工具进行了综述,给出了数据挖掘的定义、范畴和特点,以及数据挖掘的数据集的各种实际情况;总结了数据挖掘在实际应用时的基本步骤和过程;对数据挖掘在各种应用问题上的任务和建模进行了讨论;列举了目前数据挖掘领域中主要流行的算法,并对算法设计需要考虑的问题进行了简要的分析;综述了目前数据挖掘算法在一些领域的应用;较全面地叙述了目前数据挖掘软件工具性能及其开发商情况;最后,对数据挖掘的发展前景和方向进行了展望。