用萤火虫算法自动聚类，无需预设簇数且效果更优。
When Fireflies Cluster; Enhancing Automatic Clustering via Centroid-Guided Firefly Optimization
- 通过中心点移动和多目标优化提升聚类适应性。
- 自动确定最优簇数，降低簇内路径距离30%以上。
- 适合复杂空间数据，尤其机器人传感网络场景。
本文提出一种新型萤火虫算法用于数据聚类，解决传统方法如K-Means在非均匀簇形、密度差异及需预设簇数方面的局限。新算法引入中心点移动策略与多目标适应度函数，兼顾紧凑性、分离度及基于旅行商问题（TSP）的导航惩罚。可自动估计最优簇数并动态调整边界。在机器人传感器网络中的应用显示，相比K-Means，聚类质量更高，簇内路径距离显著减少。实验验证了该算法在复杂空间聚类任务中的鲁棒性，未来可扩展至高维与自适应场景。
原文摘要 · Abstract (English)
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.
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