基于遗传算法的图的划分度量维数计算

武建, 赵海霞, 杨卫华

应用数学学报 ›› 2020, Vol. 43 ›› Issue (6) : 1013-1028.

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应用数学学报 ›› 2020, Vol. 43 ›› Issue (6) : 1013-1028. DOI: 10.12387/C2020074
论文

基于遗传算法的图的划分度量维数计算

    武建1,2, 赵海霞3, 杨卫华4
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Computing the Partition Metric Dimension of Graphs Based on Genetic Algorithm

    WU Jian1,2, ZHAO Haixia3, YANG Weihua4
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摘要

G=(V,E)是简单连通图,Π={S1S2,…,Sk}是对顶点集V的一个划分.顶点vV与非空顶点子集SV的距离为dGv,S)=min{dGv,x)|xSSV}.顶点vV关于划分Π的表征是一个k-维距离向量rGv|Π)=(dGvS1),dGvS2),…,dGvSk)).若对任意两个顶点u,vVrGu|Π)≠rGv|Π)成立,则每个顶点具有唯一的k-维向量表征,并称Π是V的一个分辨划分,简称图G的分辨划分.具有最小划分数的分辨划分为图G的一个划分基.划分基所含顶点子集的个数为图G的划分度量维数,简称划分维数.图的分辨划分及划分维数问题是由Chartrand提出的一类NP-困难问题.本文基于遗传算法研究一般图的划分维数计算问题,刻画了图的分辨划分内在的拓扑结构;采用个体离散实值编码技术,个体划分分裂修补技术,设计了能够计算图的划分维数和分辨划分的遗传算法;数值计算表明,算法在二维网格图上计算准确率较高,并为凸多胞形的划分维数找到了最优上界,在随机图上运行较为有效.

Abstract

Let G=(V, E) be a simple and connected graph with a partiton Π={S1, S2, …, Sk} of V. The distance between a vertex vV and a non-empty subset SV is defined as dG(v,S)=min{dG(v,x)|xS, SV}. Let rG(v|Π)= (dG(v,S1), dG(v,S2), …, dG(v,Sk)) denote the k-distance vector of vV to the partition Π. For any two vertices u, vV, if rG(u|Π)≠rG(v|Π), then we say that each vertex of V has an unique k-distance vector representation, and Π is a resolving partition of G. The resolving partition with the minimum number of subsets of V is a partition base of G, accordingly the minimum number is the partition dimension of G. The resolving partition and partition dimension problem is an NP-hard problem proposed by Chartrand in 2000. Based on the genetic algorithm, we study the method to compute the partition dimension of general connected graphs. In this paper, we characterise the internal topology structure of a resolving partition. Using the technique of discrete real-value coding and the technique of individual repairation, a genetic algorithm which can provide partition dimension and resolving partition for graphs is designed. The algorithm is executed on two dimensional grids, convex polytopes and random graphs, the results of which show that the algorithm has a good performance on the two-dimensional grid with a hither computation accuracy, and can find the optimal upper bound of partition dimension for convex polytopes. The algorithm runs on random graphs well and effectively with shorter average iteration time, computing the optimal partition dimensions and resolving partition for graphs.

关键词

距离 / 分辨划分 / 划分维数 / 遗传算法 / 个体修复

Key words

distance / resolving partition / partition dimension / genetic algorithm / individual repairation

引用本文

导出引用
武建, 赵海霞, 杨卫华. 基于遗传算法的图的划分度量维数计算. 应用数学学报, 2020, 43(6): 1013-1028 https://doi.org/10.12387/C2020074
WU Jian, ZHAO Haixia, YANG Weihua. Computing the Partition Metric Dimension of Graphs Based on Genetic Algorithm. Acta Mathematicae Applicatae Sinica, 2020, 43(6): 1013-1028 https://doi.org/10.12387/C2020074

参考文献

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基金

国家自然科学基金(11671296)资助项目.
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