Bicriteria network design using a spanning tree-based genetic algorithm

The optimization problems in communication networks have received the attention of many researchers in such related fields as network designer, network analysis, and network administration. The use of computer communication networks has been increasing rapidly in order to share expensive hardware/software resources and provide access to main systems from distant locations. These network problems have many applications in telecommunications, computer networking, and related domains in electric, gas, and sewer networks. In computer networking, LANs (local area networks) are commonly used as the communication infrastructure that meets the demands of users in the local environment. These networks typically consist of several LAN segments connected together via bridges. The use of these transparent bridges requires.loop-free paths between LAN segments. Therefore, only spanning tree topologies can be used as active LAN configurations. Recently, genetic algorithms have greatly advanced in related research fields such as network optimization problems, combinatorial optimization, multiobjective optimization, and so on. Genetic algorithms have also received a great deal of attention because of their ability as optimization techniques for many real-world problems. In this paper, we attempt to solve the LAN topology design problem with bicriteria which minimize the cost and average message delay using genetic algorithms, and propose a method of searching the Pareto solutions. We also employ the Prüfer number in order to represent the chromosomes, because the interconnection between the network service centers must yield spanning tree configurations. Finally, we conduct experiments to certify the quality of the networks designs obtained by using genetic algorithms. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

基于改进生成树优化算法的抗毁性网络设计研究

针对当前通信网络抗毁性设计问题,以连通度和跳数作为评价指标,建立了满足指标约束条件且成本开销最小化的网络优化设计模型,并在此基础上提出了改进生成树优化算法求解该模型。仿真结果表明,该算法与生成树优化算法相比,能够更好地权衡各项指标,在确保抗毁性条件下可有效降低成本开销。对于通信网络,特别是大型网络的规划及优化设计,该算法具有实际应用价值和可操作性。     

The multi-criteria minimum spanning tree problem based genetic algorithm

Minimum spanning tree (MST) problem is of high importance in network optimization and can be solved efficiently. The multi-criteria MST (mc-MST) is a more realistic representation of the practical problems in the real world, but it is difficult for traditional optimization technique to deal with. In this paper, a non-generational genetic algorithm (GA) for mc-MST is proposed. To keep the population diversity, this paper designs an efficient crossover operator by using dislocation a crossover technique and builds a niche evolution procedure, where a better offspring does not replace the whole or most individuals but replaces the worse ones of the current population. To evaluate the non-generational GA, the solution sets generated by it are compared with solution sets from an improved algorithm for enumerating all Pareto optimal spanning trees. The improved enumeration algorithm is proved to find all Pareto optimal solutions and experimental results show that the non-generational GA is efficient.     

无线Mesh网络中多信道生成树路由算法

无线Mesh网络是一种特殊的AdHoc网络。它易于部署、安装,能有效地构建无线骨干网,通常被用作宽带Internet接入和扩展无线LAN的覆盖范围。针对无线Mesh网络的特点,提出了一种不同于一般MANET路由协议的路由算法。该算法基于网络拓扑生成树,使用多个无重叠信道;在解决信道分配问题的同时,兼顾信道多样性和信道重用,更好地利用无线频谱资源,支持链路并行传输。     

一种基于多维节点属性层次聚类的应用层组播生成树算法

为提高应用层组播生成树的稳定性和效率,提出一种基于多维节点属性层次聚类应用层组播生成树算法。针对应用多维属性定义节点稳定性存在的困难,算法根据节点多维属性计算稳定性相似度,按相似度阈值进行层次聚类,建立分层结构,并在分层结构的基础上通过最小生成树算法提高组播生成树效率。实验模拟表明,算法能有效改善组播树的非稳态结构,同时也能保证通信性能。     

A one-parameter genetic algorithm for the minimum labeling spanning tree problem

Given a connected, undirected graph G whose edges are labeled (or colored), the minimum labeling spanning tree (MLST) problem seeks a spanning tree on G with the minimum number of distinct labels (or colors). In recent work, the MLST problem has been shown to be NP-hard and an effective heuristic [maximum vertex covering algorithm (MVCA)] has been proposed and analyzed. We use a one-parameter genetic algorithm (GA) to solve the problem. In computational tests, the GA clearly outperforms MVCA.     

An oriented spanning tree based genetic algorithm for multi-criteria shortest path problems

This paper investigates an oriented spanning tree (OST) based genetic algorithm (GA) for the multi-criteria shortest path problem (MSPP) as well as the multi-criteria constrained shortest path problem (MCSPP). By encoding a path as an OST, in contrast with the existing evolutionary algorithms (EA) for shortest path problem (SPP), the designed GA provides a “search from a paths set to another paths set” mutation mechanism such that both of its local search and global search capabilities are greatly improved. Because the possibility to find a feasible path in a paths set is usually larger than that of only one path is feasible, the designed GA has more predominance for solving MCSPPs. Some computational tests are presented and the test results are compared with those obtained by a recent EA of which the encoding approach and the ideas of evolution operators such as mutation and crossover are adopted in most of the existing EAs for shortest path problems. The test results indicate that the new algorithm is available for both of MSPP and MCSPP.     

A biased random-key genetic algorithm for the capacitated minimum spanning tree problem

This paper focuses on the capacitated minimum spanning tree (CMST) problem. Given a central processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals, the goal is to design a minimum cost network to carry this demand. Potential links exist between any pair of terminals and between the central processor and the terminals. Each potential link can be included in the design at a given cost. The CMST problem is to design a minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-key genetic algorithm (BRKGA) is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem. This paper explores several solution encodings as well as different strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem. Computational experiments are presented showing the effectiveness of the approach: Seven new best-known solutions are presented for the set of benchmark instances used in the experiments.     

求解最小生成树的一种小生境遗传禁忌算法

针对最小生成树问题,提出了一种小生境遗传禁忌算法。算法中使用Prfer数对生成树进行编码。在选择交叉之前使用小生境技术,使得被选中交叉的个体之间的适应值的距离大于一定的阈值,从而保证了个体的多样性。遗传变异算子使用禁忌搜索算法,提高了遗传算法的局部搜索能力,加快了算法的收敛速度。模拟实验结果证明该算法是有效的。     

Minimum spanning tree partitioning algorithm for microaggregation

This paper presents a clustering algorithm for partitioning a minimum spanning tree with a constraint on minimum group size. The problem is motivated by microaggregation, a disclosure limitation technique in which similar records are aggregated into groups containing a minimum of k records. Heuristic clustering methods are needed since the minimum information loss microaggregation problem is NP-hard. Our MST partitioning algorithm for microaggregation is sufficiently efficient to be practical for large data sets and yields results that are comparable to the best available heuristic methods for microaggregation. For data that contain pronounced clustering effects, our method results in significantly lower information loss. Our algorithm is general enough to accommodate different measures of information loss and can be used for other clustering applications that have a constraint on minimum group size.     

C语言中嵌套选择语句的逻辑关系浅析

本文介绍了如何用分析嵌套选择语句的逻辑关系，主要方法是借助分裂图画出N-S图，最后再根据IF…ELSE…语句配对原理分析出各子句的关系。     

基于数据结构的最小生成树算法

数据结构是计算机科学的算法理论基础和软件设计的技术基础,在计算机领域中有着举足轻重的作用。本文以邻接矩阵作为图的存储结构,指出如何在计算机上实现克鲁斯卡尔算法,并分析所设计算法的时间复杂度。     

A simpler minimum spanning tree verification algorithm

The problem considered here is that of determining whether a given spanning tree is a minimal spanning tree. In 1984 Komlós presented an algorithm which required only a linear number of comparisons, but nonlinear overhead to determine which comparisons to make. We simplify his algorithm and give a linear-time procedure for its implementation in the unit cost RAM model. The procedure uses table lookup of a few simple functions, which we precompute in time linear in the size of the tree.     

基于免疫-蚁群算法的度约束最小生成树算法

针对度约束最小生成树问题,借鉴人体免疫系统的适应能力和蚁群算法的全局寻优能力,提出了一种基于免疫-蚁群算法的求解方法.该算法采用Prüfer数对树进行编码及度的改进,利用免疫算法和蚁群算法的融合提高算法的执行速度和进化效率.实验结果表明,用该算法解决度约束最小生成树问题是有效的.     

最小比率生成树的竞争决策算法

最小比率生成树是找出目标函数形式为两个线性函数比值最小的生成树,例如总代价与总收益比值最小的生成树。当不限制分母的符号时,这是一个NP-hard问题。在分析最小比率生成树数学性质的基础上,提出了最小比率生成树的竞争决策算法。为了防止算法陷入局部最优,采用edge_exchange操作来增加算法的搜索范围。为了验证算法的有效性,采用无关和相关两种策略产生测试数据,并使用Delphi 7.0实现了算法的具体步骤。     

一种基于支撑树搜索的多下一跳路由算法*

基于通过搜索支撑树定势的思想,提出了一种新型多下一跳路由算法,具体包括四种可行的实现方案。该算法选路策略灵活,通过计算网络拓扑的支撑树完成对节点的定势,可以产生到目的地的大量路径同时进行分流传输,充分利用网络资源。仿真结果表明,相对于传统单下一跳路由算法,该算法能有效地提高吞吐量,减小丢包率,提升网络整体通信性能。     

基于生成树的图像完全细化算法

图像细化是图像处理的重要环节,已有的图像细化算法较多,但都存在一些缺陷,限制了算法的使用范围。提出一种基于生成树的图像细化算法,对原图像运用形态学细化算法预处理,对中间结果的连通分支分别建立生成树,利用树的结构特征对图像中各连通分支逐个细化。对指纹图像的实验结果表明,该算法能使图像得到完全细化并能有效的去除毛刺,减小噪声干扰,还能避免交叉点处连通度冗余现象,有较强的适应性。     

Minimum spanning tree problem of uncertain random network

Minimum spanning tree problem is a typical and fundamental problem in combinatorial optimization. Most of the existing literature is devoted to the case with deterministic or random weights. However, due to lack of data, a proportion of edge weights have to be estimated according to experts’ evaluations, which may be considered as uncertain variables. This paper focuses on the case where some weights are random variables and the others are uncertain variables. The concept of an ideal chance distribution is introduced and its expression is given based on the uncertainty distributions and probability distributions. A model is formulated to find a minimum spanning tree whose chance distribution is the closest to the ideal one. Finally, a numerical example is given to illustrate the modelling idea of the study.     

基于动态信标生成树的传感器定位算法

节点定位是无线传感器网络中一个基础但十分重要的研究方向。实际应用场景中,传感器节点大多被随机部署,分布往往疏密不均。现存的定位算法对节点的分布密度没有敏感性,如果算法在节点密集区域和稀疏区域使用相同的定位策略,就会造成密度大的区域定位精度低,分布相对稀疏的区域定位率低,信标节点的能量得不到最大化利用等问题。针对这些问题,提出了一种基于节点密度进行定位的生成信标树算法（GBT）。信标节点组沿着规划好的路径对节点进行遍历,实现节点的全定位。通过与其他规划动态信标节点路径算法比较,证明了GBT算法在定位时间、定位精度和对信标节点能量的充分利用上均有所改善。