关于三部分图中3-液滴最大数目的蚂蚁算法

Q4 Engineering

Control and Cybernetics Pub Date : 2021-06-01 DOI:10.2478/candc-2021-0018

Krzysztof Schiff

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引用次数: 0

摘要

当图密度q小于1时，求d部图(d = 3)中d顶点团(d = 3)的最大个数问题是组合优化中的一个重要问题，也是众多np完全问题之一。针对这一问题，提出了一种元启发式算法，即蚁群优化算法。本文介绍了蚁群算法的新进展和实验结果。在计算机图像分析、计算机视觉应用、自动化和机器人视觉系统中都会遇到寻找最大数量的3顶点团的问题。这个问题的最优解决方案归结为在一个3部图中找到一组3顶点的团，这个集合应该具有尽可能高的基数。详细的蚁群算法可以很容易地修改为d维问题，即在d部图中找到最大数量的d顶点团。

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An ant algorithm for the maximum number of 3-cliques in 3-partite graphs

Abstract The problem of finding the maximum number of d-vertices cliques (d = 3) in d-partite graph (d = 3) when graph density q is lower than 1 is an important problem in combinatorial optimization and it is one of many NP-complete problems. For this problem a meta-heuristic algorithm has been developed, namely an ant colony optimization algorithm. In this paper a new development of this ant algorithm and experimental results are presented. The problem of finding the maximum number of 3-vertices cliques can be encountered in computer image analysis, computer vision applications, automation and robotic vision systems. The optimal solution of this problem boils down to finding a set of 3-vertices cliques in a 3-partite graph and this set should have cardinality as high as possible. The elaborated ant colony algorithm can be easily modified for d-dimensional problems, that is for finding the maximum number of d-vertices cliques in a d-partite graph.

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来源期刊

Control and Cybernetics 工程技术-计算机：控制论

CiteScore

0.50

自引率

0.00%

发文量

期刊介绍： The field of interest covers general concepts, theories, methods and techniques associated with analysis, modelling, control and management in various systems (e.g. technological, economic, ecological, social). The journal is particularly interested in results in the following areas of research: Systems and control theory: general systems theory, optimal cotrol, optimization theory, data analysis, learning, artificial intelligence, modelling & identification, game theory, multicriteria optimisation, decision and negotiation methods, soft approaches: stochastic and fuzzy methods, computer science,