  Biologically inspired computing techniques are very effective and useful in
many areas of research including data clustering. Ant clustering algorithm is a
nature-inspired clustering technique which is extensively studied for over two
decades. In this study, we extend the ant clustering algorithm (ACA) to a
hybrid ant clustering algorithm (hACA). Specifically, we include a genetic
algorithm in standard ACA to extend the hybrid algorithm for better
performance. We also introduced novel pick up and drop off rules to speed up
the clustering performance. We study the performance of the hACA algorithm and
compare with standard ACA as a benchmark.
