Improved ant-based clustering and sorting in a document retrieval interface

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 in-troduce some modications that yield signicant improvements in terms of both quality and eÆciency. Firstly, we re-examine their capability to si-multaneously perform a combination of clustering and multi-dimensional scaling. In contrast to the assumptions made in earlier literature, our re-sults suggest that these algorithms perform scaling only to a very limited degree. We show how to improve on this by some modications of the algorithm and a hybridization with a simple pre-processing phase. Sec-ondly, 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.