When Fireflies Cluster; Enhancing Automatic Clustering via Centroid-Guided Firefly Optimization
For practitioners needing automatic clustering in complex spatial tasks, this work offers an incremental improvement over K-Means by handling non-uniform clusters and estimating cluster count automatically.
The paper introduces a centroid-guided firefly optimization algorithm for automatic clustering that estimates the optimal number of clusters and improves clustering quality, achieving reduced intra-cluster path distances compared to K-Means in robotic sensor network applications.
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.