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An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis

Hakam Singh, Yugal Kumar

Source Title: International Journal of Applied Metaheuristic Computing (IJAMC)13(1)

ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799885405|DOI: 10.4018/IJAMC.2022010108

MLA

Singh, Hakam, and Yugal Kumar. "An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis." IJAMC vol.13, no.1 2022: pp.1-25. http://doi.org/10.4018/IJAMC.2022010108

APA

Singh, H. & Kumar, Y. (2022). An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis. International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-25. http://doi.org/10.4018/IJAMC.2022010108

Chicago

Singh, Hakam, and Yugal Kumar. "An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis," International Journal of Applied Metaheuristic Computing (IJAMC) 13, no.1: 1-25. http://doi.org/10.4018/IJAMC.2022010108

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Abstract

Clustering is an unsupervised machine learning technique that optimally organizes the data objects in a group of clusters. In present work, a meta-heuristic algorithm based on cat intelligence is adopted for optimizing clustering problems. Further, to make the cat swarm algorithm (CSO) more robust for partitional clustering, some modifications are incorporated in it. These modifications include an improved solution search equation for balancing global and local searches, accelerated velocity equation for addressing diversity, especially in tracing mode. Furthermore, a neighborhood-based search strategy is introduced to handle the local optima and premature convergence problems. The performance of enhanced cat swarm optimization (ECSO) algorithm is tested on eight real-life datasets and compared with the well-known clustering algorithms. The simulation results confirm that the proposed algorithm attains the optimal results than other clustering algorithms.