传感器网络分簇时间跨度优化聚类算法

针对无线传感器网络（WSN）簇头节点能效低、网络能量负载不均衡问题,提出一种传感器网络分簇时间跨度优化（CTSO）聚类算法。该算法首先在簇头选举方式上关注了簇内成员数量和簇头间距的约束问题,尽可能地避免各个簇之间发生覆盖重叠,优化簇内节点能量;接着对簇头的选举周期进行优化,以任务执行周期大小作为一个时间跨度并分为多个轮,通过最小化簇头选举的轮数来减少用于选择簇头而花费在广播消息上的能量,提升簇头节点的能量利用率。实验仿真结果表明,对比基于多Agent的同质态数据汇聚路由方案以及自适应数据汇聚路由策略,CTSO算法的平均能量效率分别提高了62.0%和138.4%,节点寿命则分别提高了17%和9%。CTSO算法在提升无线传感器网络簇头能效及均衡节点能量上具有较好的效果。     

Multi-source homogeneous data clustering for multi-target detection from cluttered background with misdetection

This paper investigates a particular data mining problem which is to ‘identify’ an unknown number of targets based on homogeneous observations that are collected via multiple independent sources. This particular clustering problem corresponds to a significant problem of multi-target detection in the multi-sensor/scan context. No prior information is given about either the level of clutter (namely noisy data) or the number of targets/clusters, both of which have to be learned online from the data. In addition, the data-points from the same source cannot be grouped into the same cluster (namely the cannot link, CL, constraint) and the sizes of the generated clusters need to be bounded by the number of data sources. In the proposed approach, a density-based clustering mechanism is proposed firstly to identify dense regions as clusters and to remove clutter at the coarser level; the CL constraint is then applied for finer data mining and to distinguish overlapping clusters. Illustrative datasets are employed to demonstrate the validity of the present clustering approach for multi-target detection and estimation in cluttered environments which are affected by both misdetection and clutter.     

粒子寻优和最小生成树聚类下的WSN能量优化

为了均衡分簇无线传感器网络节点能量负载,提高网络的能量利用效率,提出了一种粒子寻优和最小生成树聚类规则的能量优化算法（OMST）。该算法为了使得簇头的能量负载能够得到均衡,采用基于粒子寻优的方法来进行适应值求解,通过适应值对比来求得最佳簇头,以减少簇内节点的传输能耗。同时,提出一种最小生成树聚类规则的簇首数量选择方法,该方法基于剩余能量和距离因素来选择最优的簇首数量,在保证数据传输质量的同时最小化网络总能量的消耗量。仿真结果表明,相比一种新型差分进化的无线传感器网络聚类算法和多层节能及距离感知的无线传感器网络聚类算法,OMST算法的节点平均能量效率分别提高了16.7%和6.4%,网络节点存活数量分别提高了24.1%和13.7%。     

一种基于克隆网络聚类的入侵检测方法

将免疫克隆策略用于网络结构的聚类中,能够得到克隆网络对数据进行合理的聚类分析。采用克隆网络对入侵检测数据进行学习,即用一个小规模网络来表示海量数据,完成数据的压缩表示。再利用图论中的最小生成树对克隆网络的结构进行聚类分析,从而获得描述正常行为和异常行为的数据特征,实现合理的聚类。该算法可实现对大规模无标识原始数据的入侵检测,区分正常和异常行为,并能检测到未知攻击。在KDD CUP99数据集中进行了对比仿真实验,实验结果表明：相对于以前的算法,该算法较大地提高了对已知攻击和未知攻击的入侵检测率,并降低了误警率。     

复杂网络聚类特征层次布局算法

网络聚类模式发现是网络分析中的一项重要任务,好的网络布局应能体现网络中的聚类特征,并允许用户从不同层次探索网络结构。为此,基于社团划分和多层次布局思想提出了聚类特征层次布局算法。首先利用种子节点和个性化PageRank对网络实现社团划分;其次根据划分结果对网络进行粗化,并设计了粗化网络初始布局;然后利用节点度信息改进力导向模型以完成细化;最后,为验证所提算法的有效性,设计了从整体到局部的实验。实验表明所提算法能够在有效时间内生成高质量的布局结果,与现有布局算法相比,所提算法更能真实展示网络聚类特征,同时兼顾网络微观结构,能够满足用户从不同层次探索网络结构的需要。     

A data clustering algorithm for stratified data partitioning in artificial neural network

The statistical properties of training, validation and test data play an important role in assuring optimal performance in artificial neural networks (ANNs). Researchers have proposed optimized data partitioning (ODP) and stratified data partitioning (SDP) methods to partition of input data into training, validation and test datasets. ODP methods based on genetic algorithm (GA) are computationally expensive as the random search space can be in the power of twenty or more for an average sized dataset. For SDP methods, clustering algorithms such as self organizing map (SOM) and fuzzy clustering (FC) are used to form strata. It is assumed that data points in any individual stratum are in close statistical agreement. Reported clustering algorithms are designed to form natural clusters. In the case of large multivariate datasets, some of these natural clusters can be big enough such that the furthest data vectors are statistically far away from the mean. Further, these algorithms are computationally expensive as well. We propose a custom design clustering algorithm (CDCA) to overcome these shortcomings. Comparisons are made using three benchmark case studies, one each from classification, function approximation and prediction domains. The proposed CDCA data partitioning method is evaluated in comparison with SOM, FC and GA based data partitioning methods. It is found that the CDCA data partitioning method not only perform well but also reduces the average CPU time.     

Plantwide optimization via real-time optimization with persistent parameter adaptation

Determining the optimization scope is a major issue whenever implementing Real-time Optimization (RTO). Ideally, the optimization problem should encompass the whole plant and not a single unit, which represents only a local subset of the problem. However, if the standard RTO method, the two-step approach (TS), is applied to the entire plant, the whole system needs to be at steady-state (SS) in order to initiate the optimization cycle. This condition is rarely found in practice. One alternative is to apply Real-time Optimization with Persistent Parameter Adaptation (ROPA). ROPA is an RTO variant that integrates online estimators to the standard TS framework and avoids the need of waiting for steady-state to trigger the optimization cycle. However, the problem shifts to obtaining a dynamic model of the entire plant, which can be challenging and time consuming. This paper proposes a variant of ROPA, named asynchronous ROPA (asROPA), where the plant-wide model is partitioned into submodels and, depending on their characteristics, their parameters are updated using either online or steady-state estimators. Consequently, it is not necessary to obtain a dynamic model for the whole process. This asynchronous updating strategy allows the plant-wide model to be up-to-date to the process and the plant-wide optimization can be scheduled at any arbitrary time. The new strategy is applied to a case study consisting of a system whose model can be partitioned into a separation and a reaction submodel. The plant-wide results indicate that asROPA reacts much faster to the disturbances in comparison to the TS approach, improving the overall economic performance and is able to drive the system to the plant-wide optimum. Additionally, a strategy for partitioning the process and choosing the estimation strategy for each partition is proposed.     

考虑客户聚类与产品回收的两级闭环物流网络选址-路径优化

针对目前不合理的废旧产品回收以及物流活动产生的碳排放污染，提出了一种考虑客户聚类与产品回收的两级闭环物流网络选址-路径优化模型。首先，结合实际物流网络的动态性假设客户需求量和回收率的不确定性特征，以最小运营成本和最小环境影响为目标建立选址-路径优化模型；其次，对多目标进化算法进行改进，提出了考虑客户聚类结果的两级物流设施选址-路径问题求解算法；最后，对该优化算法进行算法性能分析，并以重庆市某企业为例进行了模型和算法验证。结果表明，所建立的模型和算法能有效降低决策难度并提高物流系统的运作效率，所求出的优化方案能减少物流运作成本和降低物流运输过程对环境的影响。     

Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization

In many real-world problems involving pattern recognition, system identification and modeling, control, decision making, and forecasting of time-series, available data are quite often of uncertain nature. An interesting alternative is to employ type-2 fuzzy sets, which augment fuzzy models with expressive power to develop models, which efficiently capture the factor of uncertainty. The three-dimensional membership functions of type-2 fuzzy sets offer additional degrees of freedom that make it possible to directly and more effectively account for model’s uncertainties. Type-2 fuzzy logic systems developed with the aid of evolutionary optimization forms a useful modeling tool subsequently resulting in a collection of efficient “If-Then” rules.The type-2 fuzzy neural networks take advantage of capabilities of fuzzy clustering by generating type-2 fuzzy rule base, resulting in a small number of rules and then optimizing membership functions of type-2 fuzzy sets present in the antecedent and consequent parts of the rules. The clustering itself is realized with the aid of differential evolution.Several examples, including a benchmark problem of identification of nonlinear system, are considered. The reported comparative analysis of experimental results is used to quantify the performance of the developed networks.     

An evolutionary algorithm with directed weights for constrained multi-objective optimization

When solving constrained multi-objective optimization problems (CMOPs), keeping infeasible individuals with good objective values and small constraint violations in the population can improve the performance of the algorithms, since they provide the information about the optimal direction towards Pareto front. By taking the constraint violation as an objective, we propose a novel constraint-handling technique based on directed weights to deal with CMOPs. This paper adopts two types of weights, i.e. feasible and infeasible weights distributing on feasible and infeasible regions respectively, to guide the search to the promising region. To utilize the useful information contained in infeasible individuals, this paper uses infeasible weights to maintain a number of well-diversified infeasible individuals. Meanwhile, they are dynamically changed along with the evolution to prefer infeasible individuals with better objective values and smaller constraint violations. Furthermore, 18 test instances and 2 engineering design problems are used to evaluate the effectiveness of the proposed algorithm. Several numerical experiments indicate that the proposed algorithm outperforms four compared algorithms in terms of finding a set of well-distributed non-domination solutions.     

一种基于ODMRP的按需式分簇多播路由协议

ODMRP协议是无线自组网环境下的一种按需式多播路由协议。提出了一种对该协议的改进,即ODC-ODMRP路由协议。协议继承了ODMRP协议对拓扑频繁变化的良好适应性,并采用按需式分簇机制（ODC）,有效减少了泛洪带来的控制开销,提高了网络的路由效率。实验结果表明,该算法具有路由收敛速度快、泛洪效率高、控制开销小等特点。     

On the clustering of multidimensional pictorial data

A new approach to problems of clustering and classification of multidimensional pictorial data is presented. Proceeding logically from simple models and assumptions, the development of a clustering technique and program is described. Some tests of the program have been performed and this work is reported. The techniques make use of information from the spatial domain.     

Dynamic clustering with improved binary artificial bee colony algorithm

One of the most well-known binary (discrete) versions of the artificial bee colony algorithm is the similarity measure based discrete artificial bee colony, which was first proposed to deal with the uncapacited facility location (UFLP) problem. The discrete artificial bee colony simply depends on measuring the similarity between the binary vectors through Jaccard coefficient. Although it is accepted as one of the simple, novel and efficient binary variant of the artificial bee colony, the applied mechanism for generating new solutions concerning to the information of similarity between the solutions only consider one similarity case i.e. it does not handle all similarity cases. To cover this issue, new solution generation mechanism of the discrete artificial bee colony is enhanced using all similarity cases through the genetically inspired components. Furthermore, the superiority of the proposed algorithm is demonstrated by comparing it with the basic discrete artificial bee colony, binary particle swarm optimization, genetic algorithm in dynamic (automatic) clustering, in which the number of clusters is determined automatically i.e. it does not need to be specified in contrast to the classical techniques. Not only evolutionary computation based algorithms, but also classical approaches such as fuzzy C-means and K-means are employed to put forward the effectiveness of the proposed approach in clustering. The obtained results indicate that the discrete artificial bee colony with the enhanced solution generator component is able to reach more valuable solutions than the other algorithms in dynamic clustering, which is strongly accepted as one of the most difficult NP-hard problem by researchers.     

一种基于ODMRP的源节点分簇多播路由协议

ODMRP协议是无线自组网环境下的一种按需式多播路由协议。提出了一种对该协议的改进,即SC-ODMRP路由协议.协议继承了ODMRP协议对拓扑频繁变化的良好适应性,并采用基于多播源节点分簇机制（Source-based Clustering,SC）,大大减少了泛洪带来的控制开销,有效地提高了网络的路由效率。实验结果表明,该算法具有路由收敛速度快、泛洪效率高、控制开销小等特点。     

Border-hunting optimization for mobile agent-based intrusion detection with deep convolutional neural network

The sensor nodes, which are available in the wireless sensor networks (WSN), are equipped with sensing abilities, and communication. Several domains require the sensor nodes to be arranged in aggressive surroundings, in which observing malicious activities within the sensor network. Therefore, the present research proposes the border-hunting optimization-based deep CNN (BHO-DCNN) for the mobile agent (MA)-based intrusion detection in WSN. The importance of the research relies on the BHO-DCNN model for identifying the intrusion available in the network is established by integrating the optimization with its features through a deep classifier for detection in a precise manner. The algorithm follows the communal hierarchy, surrounding, group hunting, and prey attacking, which provides an enhanced rate of convergence in the method of detection. The analysis is achieved through the database IDS 2018 Intrusion CSVs depending on the performance like delay, alive nodes, normalized energy, as well as throughput. The obtained number of alive nodes through the developed BHO-DCNN algorithm is 45, end-to-end delay is 0.2572 ms, normalized energy is 0.1622 J, as well as throughput, is 0.3125% for nodes 50 at the populate rate of 100, respectively.     

基于模糊聚类神经网络的镜头突变检测算法

讨论了采用无监督的模糊竞争学习算法,并结合自组织竞争网络构成的一种新型模糊聚类神经网络模型,提出了一种基于该网络模型的镜头突变检测算法。该算法通过对线性特征空间进行由粗到细的两步模糊聚类实现镜头突变的检测。实验结果表明该算法是可行和有效的。     

融合全局结构信息的拓扑优化图卷积网络

基于拓扑优化的图卷积网络(TOGCN)是一类图卷积神经网络(GCNN)模型,它通过网络中的辅助信息优化网络拓扑结构,有利于反映节点间的联系程度;然而TOGCN模型仅注重局部节点之间的关联关系,对网络潜在的全局结构信息关注不足.融合全局特征信息,有助于提高模型的性能和处理信息缺失时的鲁棒性.提出了融合全局结构信息的拓扑优...     

Dynamic clustering using particle swarm optimization with application in image segmentation

A new dynamic clustering approach (DCPSO), based on particle swarm optimization, is proposed. This approach is applied to image segmentation. The proposed approach automatically determines the “optimum” number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions. Using binary particle swarm optimization the “best” number of clusters is selected. The centers of the chosen clusters is then refined via the K-means clustering algorithm. The proposed approach was applied on both synthetic and natural images. The experiments conducted show that the proposed approach generally found the “optimum” number of clusters on the tested images. A genetic algorithm and random search version of dynamic clustering is presented and compared to the particle swarm version.     

快速复杂网络聚类图形处理器并行算法

研究复杂网络拓扑属性的聚类算法需要处理大量节点和连接边,因此对计算性能要求高,否则无法处理现实中的表示为复杂网络的系统。利用图形处理器(GPU)的并行聚类算法是解决该问题的重要方法。利用原语技术设计并行快速聚类算法,原语法不仅降低并行算法的复杂性而且提高聚类的普适性;再从线程调度策略和缓存管理两个方面提出优化的方法来解决负载均衡和数据重用性问题。通过实验对比并行快速聚类算法与优化算法的性能,结果显示并行快速聚类优化后的算法取得较好加速比。