Hybrid Ant Swarm-Based Data Clustering
This work addresses data clustering challenges for researchers and practitioners, but it is incremental as it builds upon existing biologically inspired methods.
The authors tackled the problem of improving data clustering performance by extending the ant clustering algorithm (ACA) to a hybrid version (hACA) that incorporates a genetic algorithm and novel rules, resulting in enhanced speed and performance compared to standard ACA.
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.