When Fireflies Cluster; Enhancing Automatic Clustering via Centroid-Guided Firefly Optimization
A new variant of the Firefly Algorithm has been developed to enhance data clustering capabilities. This algorithm addresses the limitations of traditional methods like K-Means by introducing a centroid movement strategy and a multi-objective fitness function. Experiments demonstrate its effectiveness in improving clustering quality, particularly in robotic sensor networks.
- ▪The proposed algorithm enhances clustering by automatically estimating the optimal number of clusters.
- ▪It incorporates a centroid movement strategy and a multi-objective fitness function for better performance.
- ▪Experiments show improved clustering quality and reduced intra-cluster path distances compared to K-Means.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18460 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.18460 (cs) [Submitted on 18 May 2026] Title:When Fireflies Cluster; Enhancing Automatic Clustering via Centroid-Guided Firefly Optimization Authors:MKA Ariyaratne, Azwirman Gusrialdi, Yury Nikulin, Jaakko Peltonen View a PDF of the paper titled When Fireflies Cluster; Enhancing Automatic Clustering via Centroid-Guided Firefly Optimization, by MKA Ariyaratne and 3 other authors View PDF HTML (experimental) 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.
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