Firefly forest: A swarm iteration-free swarm intelligence clustering algorithm
Document Type
Article
Publication Date
11-1-2024
Abstract
The Firefly Forest algorithm is a novel bio-inspired clustering method designed to address key challenges in traditional clustering techniques, such as the need to set a fixed number of neighbors, predefine cluster numbers, and rely on computationally intensive swarm iterative processes. The algorithm begins by using an adaptive neighbor estimation, refined to filter outliers, to determine the brightness of each firefly. This brightness guides the formation of firefly trees, which are then merged into cohesive firefly forests, completing the clustering process. This approach allows the algorithm to dynamically capture both local and global patterns, eliminate the need for predefined cluster numbers, and operate with low computational complexity. Experiments involving 14 established clustering algorithms across 19 diverse datasets, using 8 evaluative metrics, demonstrate the Firefly Forest algorithm's superior accuracy and robustness. These results highlight its potential as a powerful tool for real-world clustering applications. Our code is available at: https://github.com/firesaku/FireflyForest.
Keywords
Swarm Intelligence, Bio-inspired Clustering, Iteration Free, Outlier Filter
Divisions
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Funders
Sichuan Comic and Animation Research Center, Key Research Institute of Social Sciences of Sichuan Province (DM2024013),National Supercomputing Center in Chengdu-Chengdu University Branch, Sichuan, China
Publication Title
Journal of King Saud University - Computer and Information Sciences
Volume
36
Issue
9
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS