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Two swarm intelligence-based approaches for the p-centre problem

机译:两种基于群体智能的方法来解决p中心问题

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The p -centre problem is an important facility location problem. In this problem, the objective is to find a set Y of p vertices on an undirected weighted graph G = ( V , E ) in such a way that Y ⊆ V and the maximum distance over all the distances from vertices to their closest vertices in Y is minimised. The vertices in set Y are called centres. In this paper, we have proposed two swarm intelligence-based approaches for the p -centre problem. The first approach is based on artificial bee colony (ABC) algorithm, whereas the latter approach is based on invasive weed optimisation (IWO) algorithm. The ABC algorithm and IWO algorithm are relatively new metaheuristic techniques inspired respectively from collective intelligent behaviour shown by honeybees while foraging and the sturdy process of weed colonisation and dispersion in an ecosystem. Computational results on the well-known benchmark instances of p -centre problem show the effectiveness of our approaches in finding high quality solutions.
机译:p中心问题是重要的设施位置问题。在此问题中,目标是在无向加权图G =(V,E)上找到p个顶点的集合Y,使得Y⊆V和从顶点到其最近顶点的所有距离上的最大距离。 Y被最小化。集合Y中的顶点称为中心。在本文中,我们针对p中心问题提出了两种基于群体智能的方法。第一种方法基于人工蜂群(ABC)算法,而第二种方法则基于入侵性杂草优化(IWO)算法。 ABC算法和IWO算法是相对较新的元启发式技术,它们分别受到蜜蜂在觅食以及杂草在生态系统中的定居和扩散的坚固过程的启发而产生的集体智能行为的启发。对p中心问题的著名基准实例的计算结果表明,我们的方法在寻找高质量解决方案方面是有效的。

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