一般t -着色问题的两种混合蚂蚁算法

IF 1.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
M. Aicha, Bessedik Malika, D. Habiba
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引用次数: 7

摘要

GCP是一个著名的组合问题,它承认从t着色(GTCP)的几个推广。给定一个图G和与G的边相关联的正整数集T, G的T着色是对其顶点的颜色分配,因此分配的颜色距离不存在于关联集T中。由于该问题是np完全的,因此在集合T上只有很少的启发式算法被实现。蚁群优化(ACO)已成功应用于不同的问题[SAL08]。然而,对于t -着色问题,还没有发表过使用蚁群算法的尝试。本文介绍了两种结合蚁群算法和禁忌搜索的混合进化算法。通过不同的参数设置,对GTCP的一般情况和限制情况进行了实验。研究结果令人鼓舞,而且往往比已发表的结果更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two hybrid ant algorithms for the general T-colouring problem
GCP is a well-known combinatorial problem that admits several generalisations from which the T-colouring (GTCP). Given a graph G and sets T of positive integers associated to the edges of G, a T-colouring of G is an assignment of colours to its vertices so the assigned colours distances do not exist in the associated set T. Since this problem is NP-Complete, only few heuristics are implemented for restricted conditions on the sets T. The ant colony optimisation (ACO) has been successfully applied to different problems [SAL08]. Nevertheless, no attempt of ACO has been published for the T-colouring problem. We introduce, in this paper, two hybrid evolutionary approaches combining an ACO algorithm and a tabu search for the GTCP. These approaches are experimented for the general and restricted cases of the GTCP with different parameter's settings. The results are encouraging and show often better results than those published.
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来源期刊
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.10
自引率
5.70%
发文量
37
审稿时长
>12 weeks
期刊介绍: IJBIC discusses the new bio-inspired computation methodologies derived from the animal and plant world, such as new algorithms mimicking the wolf schooling, the plant survival process, etc. Topics covered include: -New bio-inspired methodologies coming from creatures living in nature artificial society- physical/chemical phenomena- New bio-inspired methodology analysis tools, e.g. rough sets, stochastic processes- Brain-inspired methods: models and algorithms- Bio-inspired computation with big data: algorithms and structures- Applications associated with bio-inspired methodologies, e.g. bioinformatics.
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