Hybrid Ant Swarm-Based Data Clustering

TL;DR

This paper introduces a hybrid ant clustering algorithm that combines ant-inspired methods with genetic algorithms and novel rules to improve data clustering performance.

Contribution

It extends the standard ant clustering algorithm by integrating genetic algorithms and new pickup/drop-off rules for enhanced efficiency.

Findings

hACA outperforms standard ACA in clustering speed and accuracy

The hybrid approach demonstrates improved scalability and robustness

Experimental results validate the effectiveness of the proposed modifications

Abstract

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.