Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms

Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, each group considered as cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for computing problems. Firefly algorithm one metaheuristic regarded an tool many issues areas such clustering. To overcome velocity, firefly can integrated with popular particle swarm algorithm. this paper, two modified algorithms, namely crazy variable step size algorithm, are hybridized individually standard applied domain The results obtained by planned hybrid have been compared existing utilizing ten UCI Machine Learning Repository datasets eight Shape sets performance evaluation. addition this, validity measures, Compact-separated David–Bouldin, used analyzing efficiency these algorithms. experimental show that proposed outperform