一种基于网格划分的自适应粒子群优化算法*

为了提升粒子群优化算法(particle swarm optimization, PSO)的性能,提出了一种基于网格划分的自适应粒子群优化(grid-based adaptive particle swarm optimization, GAPSO)算法和基于网格划分的多样性计算方法,并设计了初始种群多样性最大化(maximal diversity algorithm for the initial swarm,MDAIS)算法。GAPSO分为两个阶段：根据粒子对多样性的贡献自适应调整粒子的进化方向,使种     

一种更简化而高效的粒子群优化算法

针对基本粒子群优化(basic particle swarm optimization,简称bPSO)算法容易陷入局部极值、进化后期的收敛速度慢和精度低等缺点,采用简化粒子群优化方程和添加极值扰动算子两种策略加以改进,提出了简化粒子群优化(simple particle swarm optimization,简称sPSO)算法、带极值扰动粒子群优化(extremum disturbed particle swarm optimization,简称tPSO)算法和基于二者的带极值扰动的简化粒子群优化(ext     

位置修复和粒子置换的FSUD-PSO签名网络社区发现

为提高签名网络社区发现效果,解决其评估指标存在的数据耦合和依赖性,造成网络社区单指标优化存在较大局限性的问题,提出了基于位置修复和粒子置换的FSUD-PSO(fast sorting and uniform density of multi-objective particle swarm optimization)签名网络社区发现算法。首先,对签名网络模型进行研究,并在考虑数据耦合和依赖性前提下给出签名网络社区评价指标,以及多目标Pareto最优目标模型;其次,构建签名网络模型的多目标优化粒子编码与更新规则,并根据签名网络特点设计了位置修复和粒子置换策略,同时为提高多目标粒子群算法性能,设计了快速排序均匀密度的多目标粒子群算法FSUD-PSO;最后,通过标准测试集实验对比,验证了所提FSUD-PSO签名网络社区发现算法的有效性。     

三群粒子群优化算法及其在丙烯腈收率软测量中的应用

提出了一种三群粒子群优化算法（THSPSO, three sub-swarms particle swarm optimization）．该算法将整个粒子群分为三群，第一群粒子朝全局历史最优方向飞行，第二群粒子朝着相反方向飞行，第三群粒子在全局历史最优位置周围随机飞行．分别将该算法和基本粒子群优化算法（PSO, particle swarm optimization）用于一些常用测试函数的优化问题；结果表明,与PSO相比，THSPSO具有更好的优化性能．然后，用THSPSO训练神经网络，并将其用于丙烯腈收率软测量建模，结果显示了三群粒子群优化算法在丙烯腈软测量建模中的可行性与有效性．     

一种基于IBPSO的SDN控制器放置优化方案

提出了改进离散粒子群优化（improved binary particle swarm optimization,IBPSO）算法用来解决控制器放置问题。该算法基于粒子群的全局最优和单个粒子的个体最优来决定粒子当前取值概率,消除粒子当前值对下一步迭代的影响,从而加快收敛速度,找到更优的最终结果。仿真结果表明,与离散粒子群优化（binary particle swarm optimization,BPSO）算法相比,由该算法得出的控制器放置方案在实现控制器负载均衡的同时,还可以显著降低控制器的数量。     

一种动态改变学习因子的简化粒子群算法

基本粒子群优化算法(basic particle swarm optimization,简称bPSO)具有容易陷入局部极值,进化后期熟练速度慢,精度低等缺陷,而简化粒子群算法(simple particle swarm optimization,简称sPSO)在保证了熟练速度和精度的同时舍弃了速度项,使算法更加简练。本文提出了一种动态改变学习因子的简化粒子群算法。经过实验证明,该算法在寻优精度和收敛速度上具有明显的优势。     

总结性自适应变异的粒子群算法

针对粒子群优化(particle swarm optimization,PSO)算法在迭代期间易陷入局部最优及寻优精度不高的缺点,提出一种总结性自适应变异的粒子群算法SCVPSO(self-conclusion and self-adaptive variation particle swarm optimizatio...     

改进粒子群优化算法的移动机器人路径规划

针对单一智能优化算法求解机器人路径规划时易陷入局部误区的问题,提出改进粒子群优化算法(GB_PSO)用于机器人路径规划.该算法以粒子群优化算法(particle swarm optimization,PSO)为主体,由于遗传算法(genetic algorithm,GA)和细菌觅食算法(bacterial foraging optimization algorithm,BFO)更新策略所受环境影响的不同,拟合两种环境参数;然后计算粒子与不同环境参数之间的相关性将粒子群划分为两类,分别通过GA的选择、交叉、变异算子和BFO的趋化操作并行加强局部优化;最后通过改进的粒子群更新公式对粒子进行更新,实现机器人全局和局部路径的优化.实验结果表明,改进粒子群优化算法进行路径规划提高了局部和整体的搜索能力,路径规划速度快且路径距离短,同时具备更强的鲁棒性.     

带有罚函数的无线传感器网络粒子群定位算法

无线传感器网络是一种没有基础设施的无线自组织网络,它在军事、环境检测和智能家居等诸多领域具有广泛的应用.在无线传感器网络的绝大多数应用中,只有当节点和被感知的物体的位置是可知的,节点获得的信息才有意义.因此,节点定位技术是无线传感器网络的关键技术之一.近年来,粒子群优化算法(particle swarm optimization,PSO)等智能算法被用于无线传感器网络节点定位技术的研究.在粒子群优化算法定位技术研究的基础上,提出的带有罚函数的无线传感器网络粒子群定位算法(particle swarm optimization with penalty function,PSOPF)利用罚函数来加快算法的收敛速度和提高定位算法的定位精度.实验结果表明,和原有的PSO定位算法相比较,PSOPF算法具有更高的定位精度和更快的收敛速度.     

网络社区发现的粒子群优化算法

从优化模块度的角度出发,提出了一种基于粒子群优化的网络社区发现的粒子群优化算法（CDPSO）;该算法根据网络连接数据的特点给出一种新的粒子编码方法,有效地避免非法粒子的产生,一定程度上缓解了基于二值编码的迭代二划分策略所遭遇的局部最优划分问题,并改进了传统离散粒子群优化（PSO）的粒子位置调整策略,使算法收敛速度更快.实验结果表明,CDPSO能够在无先验信息的条件下快速有效地揭示网络内在的社区结构.     

基于连边相似度的重叠社区发现算法研究

针对GN算法在发现重叠社区时存在的不足,以及为了降低算法时间复杂度,提出一种基于网络图中连边相似度划分连边集的重叠社区发现算法EGN。算法依据网络图的连边集进行划分,每一条边被划分到某个特定的社区,而一个节点可以关联多条连边,因此节点可以被划分到不同的社区,从而发现重叠社区。EGN算法首先需要构造网络节点之间连边关系的边图;然后根据边图中节点的关系计算网络图中连边的相似度,在节点之间相似度的基础上提出了连边之间相似度的计算方法;再按照相似度由小到大对边图删除边,构建出边图的树状图。树状图的每一层对应网络的一个划分,采用划分密度函数来衡量划分的质量,以此寻找最优的划分。最后将算法应用到Zachary空手道俱乐部网络中,并与GN算法进行对比,实验结果表明EGN算法能够很好地发现重叠社区。     

True-link clustering through signaling process and subcommunity merge in overlapping community detection

To overcome the difficulty in detecting reliable overlapping communities in complex networks, “true-link” and “pseudo-link” are firstly proposed on the basis of the original network graph. Then, the “true-link” graph is obtained through the preprocessing of the original network graph. And then the line graph is partitioned by means of signaling process and single-linkage hierarchical clustering. Meanwhile, the subcommunities are merged based on the proposed similarity between communities, which eradicates the inherently redundant overlapping communities to a certain extent. Compared with other overlapping community detection algorithms, this proposed algorithm is of strong robustness and high accuracy. All the results of the experiments boil down to the conclusion that this True-link Clustering Community Detection is an overlapping community detection algorithm prevailing over others.

     

Dual modularity optimization for detecting overlapping communities in bipartite networks

Many algorithms have been designed to discover community structure in networks. These algorithms are mostly dedicated to detecting disjoint communities. Very few of them are intended to discover overlapping communities, particularly the bipartite networks have hardly been explored for the detection of such communities. In this paper, we describe a new approach which consists in forming overlapping mixed communities in a bipartite network based on dual optimization of modularity. To this end, we propose two algorithms. The first one is an evolutionary algorithm dedicated for global optimization of the Newman’s modularity on the line graph. This algorithm has been tested on well-known real benchmark networks and compared with several other existing methods of community detection in networks. The second one is an algorithm that locally optimizes the graph Mancoridis modularity, and we have adapted to a bipartite graph. Specifically, this second algorithm is applied to the decomposition of vertices, resulting from the evolutionary process, and also characterizes the overlapping communities taking into account their semantic aspect. Our approach requires a priori no knowledge on the number of communities searched in the network. We show its interest on two datasets, namely, a group of synthetic networks and real-world network whose structure is also difficult to understand.     

LinkLPA: A Link‐Based Label Propagation Algorithm for Overlapping Community Detection in Networks

Community detection is an important methodology for understanding the intrinsic structure and function of complex networks. Because overlapping community is one of the characteristics of real‐world networks and should be considered for community detection, in this article, we propose an algorithm, called link‐based label propagation algorithm (LinkLPA), to detect overlapping communities. Because the link partition is conceptually natural for the problem of overlapping community detection, LinkLPA first transforms node partition problem into link partition problem and employs a new label propagation algorithm with preference on links instead of nodes to detect communities due to the simplicity and efficiency of label propagation algorithm. Then the proposed LinkLPA performs a postprocessing to refine the detected overlapping communities by avoiding over‐overlapping and incorrect partition of weak ties. Experimental results on a large number of real‐world and synthetic networks show that the proposed method achieves high accuracy on detecting overlapping communities in networks.     

基于边图的线性流重叠社区发现算法

重叠网络的社区发现是复杂网络研究中的重要问题。为了提高网络中重叠社区发现的时间效率，提出一种基于边图的线性流重叠社区发现算法LBSA。算法首先对于边图网络中的边进行随机的依次处理，完成节点的初步社区划分，再将其中重叠小社区合并到相似度最大的其他大社区中得到最终的社区。通过以上步骤，算法能够以接近线性的时间复杂度得到网络的重叠结构。从最终的实验结果来看，与其他算法相比，该算法能够在更短的时间有质量地发现网络中的重叠社区。     

异构计算中一种图的非均衡划分算法

现有的图的划分算法大多是均衡划分，要求划分块的权值相等，划分块之间的连接代价尽量最小。但是在异构计算环境中，不同的处理机的计算能力不尽相同，从而在并行任务调度时所分配的计算任务量也应随之不同。所以为了适应更广泛意义上的异构负栽均衡，本文提出了异构计算中的一种任务图的非均衡划分算法。该算法根据任意给定的需求，使得划分好的各个子集权值不均等。其中划分子集的个数等于异构环境中处理机的个数，各子集的大小比例于不同处理机的计算能力。算法包括3步：粗化阶段、非均衡划分阶段以及精化还原阶段。本文通过用格林威治大学提供的系列开放图来测试该算法，实验结果表明算法是准确有效的。     

TSCAN:利用并行策略改进的图结构聚类算法

近年来,图数据聚类在学术界引起了广泛的关注,许多优秀的聚类方法,如模块度优化算法、谱聚类,以及基于密度的聚类算法在图数据上取得了很好的效果。SCAN是一种著名的基于密度的图聚类算法,该算法不仅能够找出图中的聚类,而且还能够发现不同聚类间的Hub节点,以及图中的离群点。然而,该算法存在两方面的局限性：首先,在大规模图数据上,该算法需要耗费大量的时间用于计算图中每条边的结构相似性;另一方面,该算法存在两个参数[ε]和[μ],并且对这两个参数比较敏感。为了解决其局限性,提出了一种基于OpenMP的并行算法来求解节点相似性,并且提出了两种有效的负载均衡策略;其次,提出一种基于三角形的新型图结构聚类算法TSCAN。该模型能够有效降低算法对参数的敏感性,而且还能够发现重叠以及更稠密的社区。在多个大规模数据集上实验发现,基于多核的并行算法能够达到近乎线性的加速比,而且TSCAN算法对参数不敏感,能有效发现重叠社区。     

用于网络重叠社区发现的粗糙谱聚类算法

针对绝大多数社区发现算法都存在着网络节点仅隶属于一个社区的假设,引入谱图理论与粗糙集理论来分析复杂网络社区,提出一种用于网络重叠社区发现的粗糙谱聚类算法RSC,该算法用上下近似来刻画网络节点的社区归属,边界表示社区之间共享的节点,通过优化重叠社区结构模块度来实现重叠社区发现.通过3个不同类型真实网络的仿真实验,结果验证了该方法的可行性与有效性.     

LPX: Overlapping community detection based on X‐means and label propagation algorithm in attributed networks

Traditional community detection methods in attributed networks (eg, social network) usually disregard abundant node attribute information and only focus on structural information of a graph. Existing community detection methods in attributed networks are mostly applied in the detection of nonoverlapping communities and cannot be directly used to detect the overlapping structures. This article proposes an overlapping community detection algorithm in attributed networks. First, we employ the modified X‐means algorithm to cluster attributes to form different themes. Second, we employ the label propagation algorithm (LPA), which is based on neighborhood network conductance for priority and the rule of theme weight, to detect communities in each theme. Finally, we perform redundant processing to form the final community division. The proposed algorithm improves the X‐means algorithm to avoid the effects of outliers. Problems of LPA such as instability of division and adjacent communities being easily merged can be corrected by prioritizing the node neighborhood network conductance. As the community is detected in the attribute subspace, the algorithm can find overlapping communities. Experimental results on real‐attributed and synthetic‐attributed networks show that the performance of the proposed algorithm is excellent with multiple evaluation metrics.     

基于广义后缀树的二分网络社区挖掘算法

近年来,二分网络的社区挖掘问题得到了极大的关注。提出了一种基于广义后缀树的二分网络社区挖掘算法。首先从二分网络的邻接矩阵中提取网络中每个节点的链接节点序列,然后构建广义后缀树。广义后缀树的每个节点表示二分网络的一个完全二分团,由此获取并调整完全二分团。通过引入二分团的紧密度得到初始的社区划分,最后再对孤立点进行处理以得到最终的社区划分。所提算法不仅能发现重叠社区,而且能得到一对多关系的社区。在人工数据集和真实数据集上的实验表明,所提算法能准确地识别二分网络中的社区个数,获得很好的划分效果。