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How can bees colour graphs

机译:蜜蜂如何颜色图

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摘要

Marriage in honey bees optimisation (MBO) is a recent evolutionary metaheuristic inspired by the bees reproduction process. Contrary to most of swarm intelligence algorithms such as ant colony optimisation (ACO), MBO uses self-organisation to mix different heuristics. In this paper, we present an MBO approach for the graph colouring problem (GCP). We propose, as worker, in our algorithm (BeesCol) one of the following methods: local search, taboo search or a proposed-based ant colony system algorithm(IACSCol). The worker intervenes at two levels; it improves initial and crossed solutions. Moreover, in BeesCol, one or several queens are generated randomly or by a specific constructive method, namely, recursive largest first or DSATUR. Experimental results on some well studied Dimacs graphs are reported. A comparison between BeesCol and some best-known algorithms for the GCP (hybrid colouring algorithm HCA, ant system and ant colony system) shows that the use of taboo search as worker in BeesCol reached most of best known results.
机译:蜜蜂优化婚姻(MBO)是受蜜蜂繁殖过程启发而发展起来的一种近代进化启发式方法。与大多数群体智能算法(例如蚁群优化(ACO))相反,MBO使用自组织来混合不同的启发式算法。在本文中,我们提出了一种用于图形着色问题(GCP)的MBO方法。作为工作人员,我们建议在算法(BeesCol)中使用以下方法之一:局部搜索,禁忌搜索或基于提议的蚁群系统算法(IACSCol)。工人分两个层次进行干预:它改善了初始和交叉解决方案。此外,在BeesCol中,随机生成一个或多个皇后或通过特定的构造方法(即递归最大优先或DSATUR)生成女王。报告了在一些经过充分研究的Dimacs图上的实验结果。 BeesCol与GCP的一些最著名算法(混合着色算法HCA,蚂蚁系统和蚁群系统)之间的比较表明,在BeesCol中使用禁忌搜索作为工作人员达到了最著名的结果。

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