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Erschienen in:
2012 | OriginalPaper | Buchkapitel
The Pachycondyla Apicalis Ants Search Strategy for Data Clustering Problems
verfasst von : Djibrilla Amadou Kountché, Nicolas Monmarché, Mohamed Slimane
Erschienen in: Swarm and Evolutionary Computation
Verlag: Springer Berlin Heidelberg
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This paper presents a work inspired by the
Pachycondyla apicalis
ants behavior for the clustering problem. These ants have a simple but efficient prey search strategy: when they capture their prey, they return straight to their nest, drop off the prey and systematically return back to their original position. This behavior has already been applied to optimization, as the API meta-heuristic. API is a shortage of
api-calis
. Here, we combine API with the ability of ants to sort and cluster. We provide a comparison against Ant clustering Algorithm and K-Means using Machine Learning repository datasets. API introduces new concepts to ant-based models and gives us promising results.