An Effective Crow Search Algorithm and Its Application in Data Clustering

In today’s data-centric world, the significance of generated data has increased manifold. Clustering the data into a similar group is one of the dynamic research areas among other data practices. Several algorithms’ proposals exist for clustering. Apart from the traditional algorithms, researchers worldwide have successfully employed some metaheuristic approaches for clustering. The crow search algorithm (CSA) is a recently introduced swarm-based algorithm that imitates the performance of the crow. An effective crow search algorithm (ECSA) has been proposed in the present work, which dynamically attunes its parameter to sustain the search balance and perform an oppositional-based random initialization. The ECSA is evaluated over CEC2019 Benchmark Functions and simulated for data clustering tasks compared with well-known metaheuristic approaches and famous partition-based K-means algorithm over benchmark datasets. The results reveal that the ECSA performs better than other algorithms in the context of external cluster quality metrics and convergence rate.