大规模WSNs中多Sink节点优化部署遗传算法

大规模WSNs网络布局设计中,多Sink节点的选址是网络拓扑设计的关键步骤,它对于网络通信能耗的控制至关重要。提出了一种基于遗传进化算法的Sink节点优化选址算法,它利用遗传算法的全局寻优能力在有限的时间内获得问题的次优解,进而生成监测网络工作拓扑。仿真实验结果表明:与现有的启发式算法相比较,该算法所生成的网络布局结果对于全局能耗控制有明显改进。     

基于二进制具有量子行为的粒子群算法的多边形近似

提出了适合二进制搜索空间的具有量子行为的粒子群优化算法（BQPSO）。在二进制环境中重新定义粒子的位置向量及距离向量，调整了QPSO算法的进化公式。用二进制具有量子行为的粒子群算法求解平面数字曲线的多边形近似，解决了传统BPSO算法中粒子搜索范围受限的问题。用2条通用benchmark曲线进行测试，结果表明，该算法较BPSO加快了收敛速度，在相同的容忍误差和迭代次数下找到了更少顶点的多边形。     

一种基于差分进化的社团检测算法

复杂网络的社团结构分析可抽象为一个优化问题,用进化算法求解。进化类算法的一个基本问题是如何把问题的候选解编码到进化个体中。本文将索引局部邻接表示法用于社团检测进化算法的个体表示,把社团结构分析转化为一个整数优化问题。在该个体表示方法的基础上,提出了一种基于差分进化的社团检测算法。在一组合成网络和真实网络上验证了算法性能,并与两种基于遗传算法的典型社团检测进化算法进行了对比。实验结果表明,当网络社团结构较为清晰时,基于差分进化的算法检测到的社团结构具有更好的质量。     

一种基于互惠能力的P2P网络拓扑优化算法

高效的拓扑优化算法是非结构化对等网络的研究热点之一。针对现有对等网络拓扑优化算法大多基于理想的网络环境、缺乏对节点自身能力和外部环境的综合考虑的不足,给出了一种基于互惠能力的对等网络拓扑优化算法。它从节点自身能力和外部环境因素两个方面来计算节点的互惠能力,在此基础上对非结构化对等网络的拓扑结构进行优化。分析与实验结果表明,该拓扑优化算法能将互惠能力低的节点排挤到网络边缘,降低其对网络整体性能的影响,并能有效地提高对等网络的资源搜索效率。     

改进混合二进制蝗虫优化特征选择算法

特征选择是从数据集的原始特征中选出最优或较优特征子集,从而在加快分类速度的同时提高分类准确率.提出了一种改进的混合二进制蝗虫优化特征选择算法:通过引入步长引导个体位置变化的二进制转化策略,降低了进制转换的盲目性,提高了算法在解空间中的搜索性能;通过引入混合复杂进化方法,将蝗虫群体划分子群并独立进化,提高了算法的多样性,降低了早熟收敛的概率.采用改进算法对UCI部分数据集进行特征选择,使用K-NN分类器对特征子集进行分类评价,实验结果表明:与基本二进制蝗虫优化算法、二进制粒子群优化算法和二进制灰狼优化算法相比,改进算法具有较优的搜索性能、收敛性能与较强的鲁棒性,能够获得更好的特征子集,取得更好的分类效果.     

一种基于改进蜂群算法的网络重构技术

空间信息网络在给定拓扑结构和资源受限的情况下,卫星节点间如何优化链路选择,重构网络拓扑结构,使得升级后的空间信息网络具有良好的抗毁性,是非常具有研究价值的问题.本文针对空间信网络拓扑重构问题,综合考虑卫星节点之间的可见性、可连通时间和可连通度等约束条件,建立了卫星网络拓扑链路模型和节点模型并提出基于改进蜂群算法的空间信息网络拓扑重构算法.仿真实验表明,该算法在资源受限的情况下,能够兼顾改善网络的有效性和抗毁性,有效延长网络的生存时间.     

基于PPGA-MBP的神经网络优化及其应用

多物种并行进化遗传算法应用于神经网络拓扑结构的设计,开辟了新的研究领域,论文提出伪并行(PPGA-MBP)混合遗传算法,结合改进的BP算法优化多层前馈神经网络的拓扑结构。算法采用实数编码来克服传统二进制编码的精度不足问题,并设计基于层次的杂交算子允许结构相异的个体杂交重组成新的个体,适应度函数更是综合考虑了均方误差、网络结构复杂度和网络的泛化能力等因素。实验证明取得了明显的优化效果,提高了神经网络的自适应能力和泛化能力,具有全局快速收敛的性能。论文还运用该算法建立了工业增产值经济预测网络模型,将网络预测值和多项式拟合值进行了对比分析。     

遗传算法和模糊神经网络在齿轮传动中的应用

基于遗传算法优化模糊神经网络齿轮传动机构优化的新模型,首先将各参数用二进制串表示,用适合度函数衡量算法的收敛状况。然后寻找最优模糊隶属函数参数,按适值选取最后一代群体中N个可能具有全局性的进化解,分别以该进化解为初始权值,用BP神经网络进行求解,比较N个由神经网络求得最优解,从而获得全局最优解。Matlab仿真结果表明所构造的识别模型预测误差非常小。     

基于移动Agent的无线传感器网络拓扑发现机制

对监测区域中部署的传感器节点的拓扑发现是传感器网络应用的前提,它反映了传感器网络的监测能力。考虑目前拓扑发现算法中能量消耗过多、网络连通性不强等问题,文中结合移动Agent的特点,提出了一种基于移动Agent的无线传感器网络拓扑发现机制,通过建立数学模型,利用相关邻近图（relative neighborhood graph）理论生成网络拓扑。实验结果表明,基于移动Agent的拓扑发现机制相对于当前存在的拓扑发现算法具有很好的稳定性和良好的节能效果,该算法可以解决节点拓扑请求信息讨多导致过多能量消耗的问颢．     

AntiMNT：一种对抗多源网络层析成像的拓扑混淆机制

作为一种典型的网络拓扑推断方法,网络层析成像技术可以被攻击者用来准确推断目标网络的拓扑结构,进而向关键节点或链路发起有针对性的攻击行为。为了有效隐藏真实的网络拓扑结构等信息,提出了一种基于主动欺骗方式对抗多源网络层析成像探测的拓扑混淆机制AntiMNT。AntiMNT针对多源网络层析成像的探测过程,策略性地构建虚假拓扑结构,并据此混淆攻击者对目标网络的端到端测量数据,使其形成错误的拓扑推断结果。为了高效生成具有高欺骗特征的混淆网络拓扑,AntiMNT随机生成候选混淆拓扑集,并在此基础上用多目标优化算法搜索具有高安全性和可信度的最优混淆拓扑。基于几种真实网络拓扑的实验分析表明,AntiMNT可以生成高欺骗性和安全性的混淆网络拓扑,从而能够有效防御基于网络层析成像的网络侦察。     

T-GAN: A deep learning framework for prediction of temporal complex networks with adaptive graph convolution and attention mechanism

Complex network is graph network with non-trivial topological features often occurring in real systems, such as video monitoring networks, social networks and sensor networks. While there is growing research study on complex networks, the main focus has been on the analysis and modeling of large networks with static topology. Predicting and control of temporal complex networks with evolving patterns are urgently needed but have been rarely studied. In view of the research gaps we are motivated to propose a novel end-to-end deep learning based network model, which is called temporal graph convolution and attention (T-GAN) for prediction of temporal complex networks. To joint extract both spatial and temporal features of complex networks, we design new adaptive graph convolution and integrate it with Long Short-Term Memory (LSTM) cells. An encoder-decoder framework is applied to achieve the objectives of predicting properties and trends of complex networks. And we proposed a dual attention block to improve the sensitivity of the model to different time slices. Our proposed T-GAN architecture is general and scalable, which can be used for a wide range of real applications. We demonstrate the applications of T-GAN to three prediction tasks for evolving complex networks, namely, node classification, feature forecasting and topology prediction over 6 open datasets. Our T-GAN based approach significantly outperforms the existing models, achieving improvement of more than 4.7% in recall and 25.1% in precision. Additional experiments are also conducted to show the generalization of the proposed model on learning the characteristic of time-series images. Extensive experiments demonstrate the effectiveness of T-GAN in learning spatial and temporal feature and predicting properties for complex networks.     

Ensemble of Evolving Neural Networks in Classification

The ensemble of evolving neural networks, which employs neural networks and genetic algorithms, is developed for classification problems in data mining. This network meets data mining requirements such as smart architecture, user interaction, and performance. The evolving neural network has a smart architecture in that it is able to select inputs from the environment and controls its topology. A built-in objective function of the network offers user interaction for customized classification. The bagging technique, which uses a portion of the training set in multiple networks, is applied to the ensemble of evolving neural networks in order to improve classification performance. The ensemble of evolving neural networks is tested by various data sets and produces better performance than both classical neural networks and simple ensemble methods.     

Folded Petersen cube networks: new competitors for the hypercubes

We introduce and analyze a new interconnection topology, called the k-dimensional folded Petersen (FP_k) network, which is constructed by iteratively applying the Cartesian product operation on the well-known Petersen graph. Since the number of nodes in FP_k is restricted to a power of ten, for better scalability we propose a generalization, the folded Petersen cube network FPQ_n,k =Q_n×FP_k, which is a product of the n-dimensional binary hypercube (Q_n) and FP_k. The FPQ_n,k topology provides regularity, node- and edge-symmetry, optimal connectivity (and therefore maximal fault-tolerance), logarithmic diameter, modularity, and permits simple self-routing and broadcasting algorithms. With the same node-degree and connectivity, FPQ _n,k has smaller diameter and accommodates more nodes than Q _n+3k, and its packing density is higher compared to several other product networks. This paper also emphasizes the versatility of the folded Petersen cube networks as a multicomputer interconnection topology by providing embeddings of many computationally important structures such as rings, multi-dimensional meshes, hypercubes, complete binary trees, tree machines, meshes of trees, and pyramids. The dilation and edge-congestion of all such embeddings are at most two     

无线传感器网络簇级拓扑演化模型研究

复杂网络理论研究表明,复杂系统的容错能力不仅仅存在于具有冗余组件的系统之中;而且也同样存在于具有无标度(scale-free)特征的网络之中;文章借助于复杂网络理论和偏好依附机制提出一种无线传感器网络簇级拓扑演化模型;拓扑动态分析表明,该模型能够很好地体现无线传感器簇间的拓扑生长过程,由该模型演化成的无线网络拓扑具有无标度网络的性质,所以该拓扑模型具有很强的容错性。     

A learning automata-based algorithm for determination of the number of hidden units for three-layer neural networks

There is no method to determine the optimal topology for multi-layer neural networks for a given problem. Usually the designer selects a topology for the network and then trains it. Since determination of the optimal topology of neural networks belongs to class of NP-hard problems, most of the existing algorithms for determination of the topology are approximate. These algorithms could be classified into four main groups: pruning algorithms, constructive algorithms, hybrid algorithms and evolutionary algorithms. These algorithms can produce near optimal solutions. Most of these algorithms use hill-climbing method and may be stuck at local minima. In this article, we first introduce a learning automaton and study its behaviour and then present an algorithm based on the proposed learning automaton, called survival algorithm, for determination of the number of hidden units of three layers neural networks. The survival algorithm uses learning automata as a global search method to increase the probability of obtaining the optimal topology. The algorithm considers the problem of optimization of the topology of neural networks as object partitioning rather than searching or parameter optimization as in existing algorithms. In survival algorithm, the training begins with a large network, and then by adding and deleting hidden units, a near optimal topology will be obtained. The algorithm has been tested on a number of problems and shown through simulations that networks generated are near optimal.     

Topological characterization and modeling of dynamic evolving power distribution networks

Existing complex network models are either with unvaried network size or based on simple growth mechanisms which cannot accurately describe the operational dynamics and characteristics of realistic networks. In this paper, we exploit the dynamic evolving phenomenon of power distribution networks covering its growth, reconnection and shrinking characteristics from the network topology perspective, and attempt to produce a novel dynamic evolving model through introducing the locating probability and shrinking mechanism. The proposed modeling approach is assessed and validated through extensive numerical simulation experiments for a range of standard IEEE power test systems. The statistical results reveal that the node degree distribution of the power network follows the power-law distribution and the node removal probability of the network dynamic has a significant impact on the network evolvement and robustness. Such macroscopic topological findings can greatly benefit the power distribution network operators (DNOs) from many aspects, including network planning, vulnerability analysis, fault prediction and cost-effective reinforcement.     

Overlay networks: A scalable alternative for P2P

Overlay networks create a structured virtual topology above the basic transport protocol level that facilitates deterministic search and guarantees convergence. Overlay networks are evolving into a critical component for self-organizing systems. Here we outline the differences between flooding-style and overlay networks, and offer specific examples of how researchers are applying the latter to problems requiring high-speed, self-organizing network topologies.     

Bridging topology optimization and additive manufacturing

Topology optimization is a technique that allows for increasingly efficient designs with minimal a priori decisions. Because of the complexity and intricacy of the solutions obtained, topology optimization was often constrained to research and theoretical studies. Additive manufacturing, a rapidly evolving field, fills the gap between topology optimization and application. Additive manufacturing has minimal limitations on the shape and complexity of the design, and is currently evolving towards new materials, higher precision and larger build sizes. Two topology optimization methods are addressed: the ground structure method and density-based topology optimization. The results obtained from these topology optimization methods require some degree of post-processing before they can be manufactured. A simple procedure is described by which output suitable for additive manufacturing can be generated. In this process, some inherent issues of the optimization technique may be magnified resulting in an unfeasible or bad product. In addition, this work aims to address some of these issues and propose methodologies by which they may be alleviated. The proposed framework has applications in a number of fields, with specific examples given from the fields of health, architecture and engineering. In addition, the generated output allows for simple communication, editing, and combination of the results into more complex designs. For the specific case of three-dimensional density-based topology optimization, a tool suitable for result inspection and generation of additive manufacturing output is also provided.     

Flood Avoidance Mechanisms for Bridged Resilient Packet Rings

Resilient Packet Ring (RPR), or the Standard IEEE 802.17, is a new IP-based network technology proposed to replace SONET/SDH in metropolitan area networks. RPR is well-adapted to handle multimedia traffc and is effcient. However, when RPR networks are bridged, inter-ring packets, or packets with the destination on a remote RPR network other than on the source network, are flooded on the source and the destination networks, and also on the path of the intermediate networks between the source and the destination networks. This decreases the available bandwidth for other traffc in those networks and is ineffcient. As a result, we propose two solutions based on topology discovery, global topology discovery (GTD) and enhanced topology discovery (ETD), that prevent the flooding of inter-ring packets. GTD enables the bridges to determine the next-hop bridge for each destination. ETD enables the source node to determine a default ringlet, so that packets reach the next-hop bridge without flooding the source network. The proposed solutions were analyzed and the overhead bandwidth and stabilization time were shown to be bounded. Simulations performed showed that the proposed solutions successfully avoid flooding and achieve optimal effciency in the intermediate and destination networks, and in the source networks with one bridge.     

Efficient automated schematic map drawing using multiobjective mixed integer programming

We present an efficient multiobjective mixed binary linear program that automates schematic mapping for network visualization and navigation. Schematic mapping has broad applications in representing transit networks, circuits, disease pathways, project tasks, organograms, and taxonomies. Good schematic maps employ distortion while preserving topology to facilitate access to physical or virtual networks. Automation is critical to saving time and costs, while encouraging adoption. We build upon previous work, particularly that of Nöllenburg and Wolff, improving upon the computational efficiency of their model by relaxing integrality constraints and reducing the number of objectives from three to two. We also employ an efficient augmented ϵ-constraint method to assist in obtaining all Pareto optimal solutions, both supported and unsupported, for a given network. Through the Vienna Underground network and a cancer pathway, along with three numerical examples, we demonstrate the applications of our methods. Finally, we discuss future directions for research in this area.