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

• Published in: Annals of operations research Vol. 131; no. 1-4; pp. 283 - 304
• Main Authors: Shyu, Shyong Jian, Yin, Peng-Yeng, Lin, Bertrand M.T.
• Format: Journal Article
• Language: English
• Published: New York Springer Nature B.V 01.10.2004
• Subjects: Algorithms; Approximation; Heuristic; Operations research; Optimization; Optimization algorithms; Performance evaluation; Pheromones; Studies; Transition rules; Traveling salesman problem
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1023/B:ANOR.0000039523.95673.33

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. [PUBLICATION ABSTRACT]