Clustering web search results using fuzzy ants

Algorithms for clustering Web search results have to be efficient and robust. Furthermore they must be able to cluster a data set without using any kind of a priori information, such as the required number of clusters. Clustering algorithms inspired by the behavior of real ants generally meet these requirements. In this article we propose a novel approach to ant‐based clustering, based on fuzzy logic. We show that it improves existing approaches and illustrates how our algorithm can be applied to the problem of Web search results clustering. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 455–474, 2007.     

基于蚁群算法的聚类优化

为解决大型网络中的最短路径问题,基于蚁群算法进行聚类优化研究。结合蚁群算法和聚类算法,将网络分割成若干个小网络后进行处理并合成,同时在过程中直接简化网络,透明化无意义的点。实验结果表明,优化后的算法能准确获得所要求的最优解,具有较快的收敛速度。     

基于聚类的镜头边界检测算法

镜头边界检测是基于内容视频检索的重要组成部分。为从不同类型的视频中有效地检测出视频镜头边界,提出一种视频镜头边界检测算法。通过视频帧图像的颜色特征,得到视频的相似性矩阵,根据突变镜头和渐变镜头在Affinity Propagation聚类结果中的不同特点,运用双阈值法检测镜头边界。实验结果表明,该算法从视频的本身信息分布出发,能自动快速地检测出镜头边界。     

Study of delivery path optimization solution based on improved ant colony model

As a bionic optimization algorithm, ant colony algorithm has the advantages of robustness, parallel computation and easy combination and so on, which can solve complicated combinatorial optimization problems. However, the selection strategy of traditional algorithm is more random, which leads to the slow evolution speed. Therefore, an improved ant colony algorithm is proposed, which uses density peak clustering algorithm to classify sites and local optimization strategy. The simulating results of multiple TSP problems demonstrate that the improved algorithm has good optimization ability, greatly improves the quality of the solution and the speed of optimization, and overcomes the slowness and tendency of the algorithm.

     

Wasp swarm optimization of the c‐means clustering model

This paper deals with clustering by optimizing the c‐means clustering model. For some data sets this clustering model possesses many local optima, so conventional alternating optimization (AO) will produce bad results. For obtaining good clustering results, the minimization procedure has to be kept from being trapped in these local optima, for example, by stochastic optimization approaches. Recently, we showed that ant colony optimization (ACO) can be effectively applied to the c‐means clustering model. In this paper, we introduce a wasp swarm optimization (WSO) algorithm to optimize the c‐means clustering model. In experiments with four benchmark data sets, the new WSO clustering algorithm is compared with AO and ACO. For data sets leading to c‐means models without local optima, both WSO and AO perform better and faster than ACO. For data sets leading to multiple local optima, WSO clearly outperforms both AO and ACO. © 2008 Wiley Periodicals, Inc.     

一种基于蚁群算法的模糊C均值聚类

针对模糊C均值(FCM)聚类算法,在选取聚类中心点时采用随机选取易使得迭代过程陷入局部最优解,FCM算法自身并不能确定聚类个数需要人为设定,并在数据分类应用时具有了一定误差的问题,提出了一种基于蚁群算法的FCM聚类算法。该算法根据蚁群聚类算法确定模糊聚类个数和FCM算法的初始聚类中心：利用蚁群算法的全局搜索性、并行计算性等特点避免了聚类陷入局部最优解：仿真结果表明了该算法的有效性。     

Cooperation and profit allocation in two-echelon logistics joint distribution network optimization

Collaborative two-echelon logistics joint distribution network can be organized through a negotiation process via logistics service providers or participants existing in the logistics system, which can effectively reduce the crisscross transportation phenomenon and improve the efficiency of the urban freight transportation system. This study establishes a linear optimization model to minimize the total cost of two-echelon logistics joint distribution network. An improved ant colony optimization algorithm integrated with genetic algorithm is presented to serve customer clustering units and resolve the model formulation by assigning logistics facilities. A two-dimensional colony encoding method is adopted to generate the initial ant colonies. Improved ant colony optimization combines the merits of ant colony optimization algorithm and genetic algorithm with both global and local search capabilities. Finally, an improved Shapley value model based on cooperative game theory and a cooperative mechanism strategy are presented to obtain the optimal profit allocation scheme and sequential coalitions respectively in two-echelon logistics joint distribution network. An empirical study in Guiyang City, China, reveals that the improved ant colony optimization algorithm is superior to the other three methods in terms of the total cost. The improved Shapley value model and monotonic path selection strategy are applied to calculate the best sequential coalition selection strategy. The proposed cooperation and profit allocation approaches provide an effective paradigm for logistics companies to share benefit, achieve win–win situations through the horizontal cooperation, and improve the negotiation power for logistics network optimization.     

Learning cooperative linguistic fuzzy rules using the best–worst ant system algorithm

Within the field of linguistic fuzzy modeling with fuzzy rule‐based systems, the automatic derivation of the linguistic fuzzy rules from numerical data is an important task. In the last few years, a large number of contributions based on techniques such as neural networks and genetic algorithms have been proposed to face this problem. In this article, we introduce a novel approach to the fuzzy rule learning problem with ant colony optimization (ACO) algorithms. To do so, this learning task is formulated as a combinatorial optimization problem. Our learning process is based on the COR methodology proposed in previous works, which provides a search space that allows us to obtain fuzzy models with a good interpretability–accuracy trade‐off. A specific ACO‐based algorithm, the Best–Worst Ant System, is used for this purpose due to the good performance shown when solving other optimization problems. We analyze the behavior of the proposed method and compare it to other learning methods and search techniques when solving two real‐world applications. The obtained results lead us to remark the good performance of our proposal in terms of interpretability, accuracy, and efficiency. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 433–452, 2005.     

敏捷凝视卫星密集点目标聚类与最优观测规划

针对敏捷凝视卫星密集点目标观测规划问题,提出一种快速观测任务聚类策略和启发式蚁群优化算法.首先,针对敏捷凝视卫星视场范围特点,提出基于顶点度的团划分算法,解决密集点观测任务聚类问题,形成系列团观测目标,有效提高观测效率;其次,为得到最优团目标观测序列,考虑目标可见时间窗口约束以及卫星敏捷机动能力约束,构建基于多目标观测收益和姿态机动能耗的性能指标,实现能量高效的任务规划;再次,为克服传统蚁群算法易陷入局部极小值和收敛较慢的缺点,设计一种同时考虑目标点优先级、目标可见时间窗口、目标之间卫星姿态转换时间等因素的启发式蚁群算法;最后,选取大规模密集地面目标验证所提出算法的可行性和高效性.     

聚类问题的蚁群算法

文章建立了聚类分析问题模型,分析了K-均值算法、模拟退火算法和蚁群算法的优缺点,结果表明蚁群算法比较有效。     

Scalable algorithms for clustering large datasets with mixed type attributes

Clustering is a widely used technique in data mining applications for discovering patterns in underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either numeric or categorical attributes. However, datasets with mixed types of attributes are common in real life data mining applications. In this article, we present two algorithms that extend the Squeezer algorithm to domains with mixed numeric and categorical attributes. The performance of the two algorithms has been studied on real and artificially generated datasets. Comparisons with other clustering algorithms illustrate the superiority of our approaches. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1077–1089, 2005.     

基于MATLAB的自适应蚁群聚类算法研究与仿真

介绍了蚁群算法和聚类分析,针对传统蚁群聚类算法参数设置对结果影响大的缺点,提出了一种自适应蚁群聚类算法。主要通过对传统蚁群算法的参数进行简化,增加对局部相似度的运算,使蚂蚁的移动具有一定的方向性,从而降低蚂蚁移动的随机性,对待聚类模式的主成分分析再进行聚类,简化聚类的过程。最后在MATLAB上做了仿真实验,实验结果分析表明,运用该算法能得到较好的聚类结果,从而说明该算法是可行的。     

融合动态层次聚类和邻域区间重组的蚁群算法

针对蚁群算法搜索速度过慢以及解质量不足等问题,提出一种融合动态层次聚类和邻域区间重组的蚁群算法。在初始阶段,调整层次聚类阈值并按照类间距离最小合并的原则迭代至目标簇集,根据预合并系数进行簇间合并,通过蚁群系统得到小类路径并断开重组以加快算法整体收敛速度;接着使用蚁群系统对解空间进行优化,同时并行处理簇集与簇集邻域区间扩散重组,增加解的多样性,进一步固定迭代次数进行比较,若邻域区间重组解质量优于当前优化解则进行推荐处理,提高解的精度;当算法停滞时,引入调整因子降低各路径信息素之间差异以增强蚂蚁搜索能力,有助于算法跳出局部最优。实验结果表明,在面对大规模问题时,算法的精度在3%左右,该方法相比传统方法可以有效提高解的精度和收敛速度。     

改进的模糊C-均值算法在医学图像分割中的应用

针对随机选取聚类中心易使得迭代过程陷入局部最优解的缺点,提出了一种混合优化蚁群和动态模糊C-均值的图像分割方法,该方法利用蚁群算法较强处理局部极值的能力,并能动态确定聚类中心和数目.针对传统的分阶段结合遗传算法和蚁群算法的策略存在收敛速度慢,聚类精度差的问题,提出在整个优化过程综合遗传算法和蚁群算法,并在蚁群算法中引入拥挤度函数,利用遗传算法的快速性、全局收敛性提高了蚁群算法的收敛速度,同时利用蚁群算法的并行性和正反馈性提高了聚类的精确度.最后将该算法应用到医学图像分割,对比实验表明,混合算法具有很强的模糊边缘和微细边缘分割能力.     

基于蚁群的无线传感器网络能量均衡非均匀分簇路由算法

无线传感器网络(WSN)路由中,节点未充分考虑路径剩余能量及链路状况进行的路由会造成网络中部分节点网络寿命减少,严重影响网络的生存时间。为此,将蚁群优化算法与非均匀分簇路由算法相结合,提出一种基于蚁群优化算法的无线传感器非均匀分簇路由算法。该算法首先利用考虑节点能量的优化非均匀分簇方法对节点进行分簇,然后以需要传输数据的节点为源节点,汇聚节点为目标节点,利用蚁群优化算法进行多路径搜索,搜索过程充分考虑了路径传输能耗、路径最小剩余能量、传输距离和跳数、所选链路的时延和带宽等因素,最后选出满足条件的多条最优路径,完成源目的节点间的信息传输。实验表明,该算法充分考虑路径传输能耗和路径最小剩余能量、传输跳数及传输距离,能有效延长无线传感器网络的生存期。     

基于多Agent结构的自适应蚁群优化聚类算法

该文在分析蚁群优化算法多Agent结构的基础上,提出了一种新的自适应蚁群优化聚类算法。算法的多Agent分层结构为L0层agent构造解,L1层agent改进可行解,L2层agent更新信息素,更新后的信息素矩阵为下一轮解的构造提供反馈信息。算法选取变异概率p及信息素残留度ρ作为自适应参数,在演化过程中进行自动调节,较好地解决了加速收敛和停滞早熟的矛盾。实验结果验证了算法的有效性,该算法的聚类效果和运行效率优于GA和SA两种演化聚类算法。     

基于相似性算法与蚁群算法的聚类算法

由于当今的网络数据是海量的,因此科研人员对某些问题进行研究时需要将不同属性的数据从中提取出来,然而在提取这些数据之前需要将相同数据进行聚类。数据聚类的过程,也就是寻找数据最优属性的过程,然而人工蚁群就是一种寻找问题最优解的算法,因此在本文中再次将蚁群算法在聚类中进行应用。由本文提出的聚类算法可以分为两个部分,第一部分是：通过相似性算法来衡量数据之间的相似度,第二部分是：根据第一部分的计算结果,再采用蚁群算法为需要聚类的数据选择不同的聚类中心,从而对不同属性的数据进行聚类,经过以上两个过程的计算,可以实现对数据的聚类。在本文中进行数据聚类时采用的相似性度量来代替距离的计算,是本文创新点之一,采用蚁群算法在聚类过程中来选择聚类中心也是本文的创新所在。     

简化蚁群算法

针对最大最小蚂蚁系统中信息素下界难以确定以及算法性能易受同构问题影响的缺点,提出一种简化蚁群算法.信息素的上下界被限制在一个固定的区间内,不随目标函数值的更新而改变;信息素的更新量是一个与具体目标函数值无关的常数.所提出的简化算法不仅具有强不变性和平移不变性,而且算法的性能不受信息素下界的影响.针对旅行商问题的仿真实验验证了改进算法的可行性和有效性.     

Chaotic ant swarm approach for data clustering

Clustering divides data into meaningful or useful groups (clusters) without any prior knowledge. It is a key technique in data mining and has become an important issue in many fields. This article presents a new clustering algorithm based on the mechanism analysis of chaotic ant swarm (CAS). It is an optimization methodology for clustering problem which aims to obtain global optimal assignment by minimizing the objective function. The proposed algorithm combines three advantages into one: finding global optimal solution to the objective function, not sensitive to clusters with different size and density and suitable to multi-dimensional data sets. The quality of this approach is evaluated on several well-known benchmark data sets. Compared with the popular clustering method named k-means algorithm and the PSO-based clustering technique, experimental results show that our algorithm is an effective clustering technique and can be used to handle data sets with complex cluster sizes, densities and multiple dimensions.     

一种用于历史疫灾分级的退火蚂蚁聚类方法

针对历史疫灾记录量化程度低、社会关联性强的问题,提出了一种结合模拟退火和蚂蚁算法的历史疫灾分级方法。利用单只蚂蚁对疫灾数据进行自动聚类并通过模拟退火算法对聚类准则进行优化,以获得疫灾聚类的全局最优解。通过与其他聚类方法的性能对比,实验结果证明该方法具有较高的精确性和实用性。