Abstract
In this paper, we investigate heuristics in order to solve the Approximated Matching Problem (AGM). We propose a tabu search algorithm which exploits a simple neighborhood but is initialized by a greedy procedure which uses a measure of similarity between the vertices of the two graphs. The algorithm is tested on a large collection of graphs of various sizes (from 300 vertices and up to 3000 vertices) and densities. Computing times range from less than 1 second up to a few minutes. The algorithm obtains consistently very good results, especially on labeled graphs. The results obtained by the tabu algorithm alone (without the greedy procedure) were very poor, illustrating the importance of using vertex similarity during the early steps of the search process.
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Kpodjedo, S., Galinier, P., Antoniol, G. (2010). Enhancing a Tabu Algorithm for Approximate Graph Matching by Using Similarity Measures. In: Cowling, P., Merz, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2010. Lecture Notes in Computer Science, vol 6022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12139-5_11
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DOI: https://doi.org/10.1007/978-3-642-12139-5_11
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