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An Ant Colony Optimization Algorithm for the Minimum Weight Vertex Cover Problem

机译:最小权重顶点覆盖问题的蚁群优化算法

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Given an undirected graph and a weighting function defined on the vertex set, the minimum weight vertex cover problem is to find a vertex subset whose total weight is minimum subject to the premise that the selected vertices cover all edges in the graph. In this paper, we introduce a meta-heuristic based upon the Ant Colony Optimization (ACO) approach, to find approximate solutions to the minimum weight vertex cover problem. In the literature, the ACO approach has been successfully applied to several well-known combinatorial optimization problems whose solutions might be in the form of paths on the associated graphs. A solution to the minimum weight vertex cover problem however needs not to constitute a path. The ACO algorithm proposed in this paper incorporates several new features so as to select vertices out of the vertex set whereas the total weight can be minimized as much as possible. Computational experiments are designed and conducted to study the performance of our proposed approach. Numerical results evince that the ACO algorithm demonstrates significant effectiveness and robustness in solving the minimum weight vertex cover problem.
机译:给定无向图和在顶点集上定义的加权函数,最小权值顶点覆盖问题是找到一个总权重最小的顶点子集,前提是所选顶点覆盖图中的所有边。在本文中,我们介绍一种基于蚁群优化(ACO)方法的元启发式算法,以找到最小权重顶点覆盖问题的近似解。在文献中,ACO方法已成功应用于一些众所周知的组合优化问题,其解决方案可能是以相关图上路径的形式出现的。然而,最小重量顶点覆盖问题的解决方案不需要构成路径。本文提出的ACO算法结合了几个新功能,以便从顶点集中选择顶点,而总权重可以尽可能地最小化。设计并进行了计算实验,以研究我们提出的方法的性能。数值结果表明,ACO算法在解决最小权重顶点覆盖问题方面显示出显着的有效性和鲁棒性。

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