Foraging Agent Swarm Optimization with Applications in Data Clustering
This paper proposes a novel method of swarm optimization called Foraging Agent Swarm Optimization (FASO). FASO is designed to converge on multiple optima in both gradient and point-based search spaces. FASO also operates well in situations where “field optima” are desired, rather than single-point optima. The utility and effectiveness of FASO in a non-gradient search space is demonstrated in the context of data clustering, where we present Foraging Agent Swarm Clustering (FASC). FASC provides several benefits over conventional clustering, such as the ability to automatically determine the number of clusters, and strong performance in both noisy and sparse data sets. FASC is demonstrated to outperform existing methods of clustering in a variety of situations. Positive results by FASC in data clustering suggest that FASO has a promising future in other optimization applications as well.
KeywordsParticle Swarm Optimization Globular Cluster Data Cluster Sensor Range Neighboring Agent
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