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

2026-05-18

AI Summary

This work proposes an enhanced firefly optimization algorithm for automatic clustering that overcomes key limitations of traditional methods such as K-Means, which struggle with clusters of non-uniform shapes and densities and require a pre-specified number of clusters. The proposed approach integrates a centroid-guided strategy, a multi-objective fitness function balancing intra-cluster compactness and inter-cluster separation, and a navigation penalty term derived from the Traveling Salesman Problem (TSP) to dynamically refine cluster boundaries without prior knowledge of the cluster count. Evaluated on robotic sensor network data, the method significantly outperforms K-Means by simultaneously improving clustering quality and reducing intra-cluster path distances, thereby enhancing robustness in clustering complex spatial datasets.

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.

Problem

• clustering
• non-uniform clusters
• number of clusters
• cluster shape
• cluster density

Innovation

• Firefly Optimization
• Automatic Clustering
• Centroid-Guided Strategy
• Multi-objective Fitness Function
• TSP-based Navigation Penalty