随机图的邻点可区别VI-均匀全染色算法

邻点可区别[VI]-均匀全染色是指图中任意两条相邻边分配不同的颜色,且任意两个色类（点或边）的颜色个数最大相差为1,同时确保相邻顶点的色集合不同,其所用的最少颜色数称为图的邻点可区别[VI]-均匀全色数。提出了一种针对随机图的邻点可区别[VI]-均匀全染色算法,该算法依据染色条件设计了三个子目标函数和一个总目标函数,并依据交换规则逐步迭代寻优,直至染色结果满足总目标函数的要求。同时给出了详细的算法执行步骤,并进行了大量的测试和分析,实验结果表明,该算法可以高效地求出给定顶点数的图的最小邻点可区别[VI]-均匀全色数。     

随机图的正常均匀全染色算法

目前对图的均匀全染色的研究仅限于一些如完全图、正则图等特殊图,还没有发现用于研究一般简单连通图的正常均匀全染色的算法。为了研究一般图的正常均匀全染色,根据正常均匀全染色的点约束、边约束、点边约束和均匀约束四个约束规则,设计了一种新的启发式智能算法。首先,该算法确定四个子目标函数和一个总目标函数;然后,在每个子目标函数内借助染色矩阵及色补集合矩阵逐步迭代交换,直到子目标函数值为0时,子目标染色完成;最后,当每个子目标函数值都为0时,总目标函数值为0,染色成功。实验结果表明,该算法可以生成8个点以内的所有简单连通图,并能对每个生成图进行正常均匀全染色,得到其均匀全色数,且验证得对任意的正整数k,当3≤ k≤ 9时,随机图G都有k-均匀全染色。同时在20到400个点之间选取了72个图,用所提算法对其进行均匀全染色,并依据染色结果绘制了它们的点数-边密度-所需色数关系图。     

外1-平面图的均匀边染色

图[G]的[s]-均匀边[k]-染色是指用[k]种颜色对图的边进行染色,使得图[G]的每个顶点所关联的任何两种颜色的边的条数至多相差[s]。使得对于每个不小于[k]的整数[t],图[G]都具有[s]-均匀边[t]-染色的最小整数[k]称为图[G]的[s]-均匀边色数阈值。文中证明了外1-平面图的1-均匀边色数阈值最多为5,不含有相邻的3圈的外1-平面图的均匀边色数阈值最多为4,外1-平面图的2-均匀边色数阈值恰好为1。     

图的点可区别边染色算法研究

针对一般图设计了一种新型的点可区别边染色算法。该算法把概率思想和图染色相结合, 根据点可区别边染色的约束规则确立目标函数, 利用交换规则逐步寻优, 当目标函数的值满足要求时染色成功。给出详细算法步骤并进行了测试和分析, 实验结果表明该算法可以求出满足猜想的点可区别边色数。     

随机图的点可区别V-全染色算法

图[G]的点可区别V-全染色就是相邻的边、顶点与其关联边必须染不同的颜色,同时要求所有顶点的色集合也不相同,所用的最少颜色数称为图[G]的点可区别V-全色数。根据点可区别V-全染色的约束规则,设计了一种启发式的点可区别V-全染色算法,该算法借助染色矩阵及色补集合逐步迭代交换,每次迭代交换后判断目标函数值,当目标函数值满足要求时染色成功。给出了算法的详细描述、算法分析和算法测试结果,对给定点数的图进行了点可区别V-全染色猜想的验证。实验结果表明,该算法有很好的执行效率并可以得到给定图的点可区别V-全色数,并且算法的时间复杂度不超过[O(n3)]。     

图的Smarandachely邻点可区别边染色算法

为解决图的Smarandachely邻点可区别边染色问题,提出一种基于多目标优化的染色算法。针对每个子问题分别设置子目标函数向量和决策空间,在颜色迭代、顺序交换和强制交换中,子目标逐渐得到最优解,最终使总目标函数符合图的Smarandachely邻点可区别边染色要求。实验结果表明,在1 000个顶点内该算法能够正确地得到随机图的Smarandachely邻点可区别边色数。     

一种应用于完全图的点可区别强全染色新算法

设f是简单图G的一个正常k-全染色,若G中任意两点所关联的点及其关联边的颜色所构成的集合互不相同,则称f为G的K-点可区别强全染色,k中的最小值为G的点可区别强全色数。针对完全图的点可区别强全染色的特点,提出一种新算法。该算法把需要填充的颜色分为两部分:超色数和正常色数,在分别得到其染色数量和染色次数的前提下先对超色数进行染色以增强算法的收敛性。实验结果表明,该算法能有效地解决完全图的点可区别强全染色问题。     

多目标优化的图的邻点可区别均匀V-全染色算法

图的邻点可区别均匀V-全染色（AVDEVTC）是指在满足邻点可区别V-全染色的基础上,还要保证每种颜色的使用次数相差不超过1,把完成AVDEVTC所用的最少颜色称为图的邻点可区别均匀V-全色数（AVDEVTCN）。针对图的AVDEVTC问题,提出了一种基于多目标优化的染色算法。设计了一个总目标函数和四个子目标函数,在染色矩阵上通过每个点的颜色集合的迭代交换操作,使得每个子目标函数都达到最优,进而满足总目标函数的要求,完成染色。经过理论分析和实验对比表明,8个顶点以内的所有简单连通图都存在AVDEVTC,且图的AVDEVTCN介于最大度加1与最大度加2之间。实验结果表明,该染色算法能够在较短的时间内正确地计算出1000个顶点以内的图的AVDEVTCN。     

路图的Smarandachely全染色算法*

设f是简单图G的一个正常的k-全染色,若G中任意两点的点及其关联边的颜色构成的集合互不包含,则称f为G的k-Smarandachely全染色,这样的k中最小者称为G的Smarandachely全色数。针对路图的Smarandachely全染色问题,提出了一种新算法。算法采用三元组编码方式将问题进行转化,按照给定规则生成三元组队列,并对该队列内部排序进行变换调整。同时,给出两个判断函数,根据函数的值判断是否得到问题的解。实验结果表明,该算法可以有效地解决路图的Smarandachely全染色问题。     

随机图的点可区别全染色算法

点可区别全染色(VDTC)是指在满足正常全染色的基础上,还要使得图中由顶点颜色和其关联边颜色构成的顶点色集合也不同,所使用的最少颜色数称为点可区别全色数.提出了一种针对随机图的点可区别全染色算法,算法的基本思想是对图G中的边随机地进行预染色,查找存在边染色不正常的冲突集,然后根据规则逐步迭代,直至使目标函数的值满足要求,此时说明染色成功.实验结果表明,算法能够有效地求得给定点数随机图的点可区别全色数,算法时间复杂度不超过O(n3).     

Acyclic coloring of graphs of maximum degree five: Nine colors are enough

An acyclic coloring of a graph G is a coloring of its vertices such that: (i) no two neighbors in G are assigned the same color and (ii) no bicolored cycle can exist in G. The acyclic chromatic number of G is the least number of colors necessary to acyclically color G. In this paper, we show that any graph of maximum degree 5 has acyclic chromatic number at most 9, and we give a linear time algorithm that achieves this bound.     

A NEW APPROACH TO THE VERTEX COLORING PROBLEM

The vertex coloring problem is a well-known classical optimization problem in graph theory in which a color is assigned to each vertex of the graph in such a way that no two adjacent vertices have the same color. The minimum vertex coloring problem is known to be an NP-hard problem in an arbitrary graph, and a host of approximation solutions are available. In this article, a learning automata–based approximation algorithm is proposed to solve the minimum vertex coloring problem. The proposed algorithm iteratively finds the different possible colorings of the graph and compares it at each stage with the best coloring found so far. If the number of distinct colors in the chosen coloring is less than that of the best coloring, the chosen coloring is rewarded; otherwise, it is penalized. Convergence of the proposed algorithm to the optimal solution is proven. The proposed vertex coloring algorithm is compared with the well-known coloring techniques and the results show the superiority of the proposed algorithm over the others both in terms of the color set size and running time of algorithm.     

Adjacent vertex-distinguishing edge and total chromatic numbers of hypercubes

An adjacent vertex-distinguishing edge coloring of a simple graph G is a proper edge coloring of G such that incident edge sets of any two adjacent vertices are assigned different sets of colors. A total coloring of a graph G is a coloring of both the edges and the vertices. A total coloring is proper if no two adjacent or incident elements receive the same color. An adjacent vertex-distinguishing total coloring h of a simple graph G=(V,E) is a proper total coloring of G such that H(u)≠H(v) for any two adjacent vertices u and v, where H(u)={h(wu)|wu∈E(G)}∪{h(u)} and H(v)={h(xv)|xv∈E(G)}∪{h(v)}. The minimum number of colors required for an adjacent vertex-distinguishing edge coloring (resp. an adjacent vertex-distinguishing total coloring) of G is called the adjacent vertex-distinguishing edge chromatic number (resp. adjacent vertex-distinguishing total chromatic number) of G and denoted by (resp. χat(G)). In this paper, we consider the adjacent vertex-distinguishing edge chromatic number and adjacent vertex-distinguishing total chromatic number of the hypercube Qn, prove that for n?3 and χat(Qn)=Δ(Qn)+2 for n?2.     

外1-平面图的均匀点荫度

图的均匀树[k]-染色是图的一个点[k]-染色,其任何两个色类的大小相差至多为1,并且每个色类的导出子图是一个森林。使得图[G]具有均匀树[k]-染色的最小整数[k]称为图[G]的均匀点荫度。证明了每个外1-平面图的均匀点荫度至多为3,继而对于外1-平面图证明了均匀点荫度猜想。     

Location-Oblivious Distributed Unit Disk Graph Coloring

We present the first location-oblivious distributed unit disk graph coloring algorithm having a provable performance ratio of three (i.e. the number of colors used by the algorithm is at most three times the chromatic number of the graph). This is an improvement over the standard sequential coloring algorithm that has a worst case lower bound on its performance ratio of 4−3/k (for any k>2, where k is the chromatic number of the unit disk graph achieving the lower bound) (Tsai et al., in Inf. Process. Lett. 84(4):195–199, 2002). We present a slightly better worst case lower bound on the performance ratio of the sequential coloring algorithm for unit disk graphs with chromatic number 4. Using simulation, we compare our algorithm with other existing unit disk graph coloring algorithms.