Abstract
Sorting and clustering methods inspired by the behavior of real ants are among the earliest methods in ant-based meta-heuristics. We revisit these methods in the context of a concrete application and introduce some modifications that yield significant improvements in terms of both quality and efficiency. Firstly, we re-examine their capability to simultaneously perform a combination of clustering and multi-dimensional scaling. In contrast to the assumptions made in earlier literature, our results suggest that these algorithms perform scaling only to a very limited degree. We show how to improve on this by some modifications of the algorithm and a hybridization with a simple pre-processing phase. Secondly, we discuss how the time-complexity of these algorithms can be improved. The improved algorithms are used as the core mechanism in a visual document retrieval system for world-wide web searches.
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Handl, J., Meyer, B. (2002). Improved Ant-Based Clustering and Sorting in a Document Retrieval Interface. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_88
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DOI: https://doi.org/10.1007/3-540-45712-7_88
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