When Fireflies Cluster; Enhancing Automatic Clustering via Centroid-Guided Firefly Optimization

TL;DR

This paper introduces a centroid-guided firefly algorithm for data clustering that automatically determines the optimal number of clusters and improves clustering quality over traditional methods like K-Means.

Contribution

A novel firefly algorithm variant with centroid movement and multi-objective fitness for enhanced, automatic clustering without pre-defined cluster counts.

Findings

Improved clustering quality over K-Means.

Automatically estimates the optimal number of clusters.

Reduces intra-cluster path distances in robotic sensor networks.

Abstract

This work presents a novel variant of the Firefly Algorithm (FA) for data clustering, addressing limitations of traditional methods like K-Means that struggle with non-uniform cluster shapes, densities, and the need for pre-defining the number of clusters. The proposed algorithm introduces a centroid movement strategy and a multi-objective fitness function that balances compactness, separation, and a novel TSP-based navigation penalty. It automatically estimates the optimal number of clusters and dynamically adjusts cluster boundaries. Application to robotic sensor networks highlights its practical value, with experiments showing improved clustering quality and reduced intra-cluster path distances compared to K-Means. These results confirm the algorithm's robustness in complex spatial clustering tasks, with potential for future extensions to higher-dimensional and adaptive scenarios.