Journal of applied mathematics & informatics

The Korean Society for Computational and Applied Mathematics (한국전산응용수학회)
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OPTIMIZING FITNESS FUNCTION OF CUTTLEFISH OPTIMIZATION FOR CLUSTERING

  • K. KALPANARANI (Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai) ;
  • G. HANNAH GRACE (Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology Chennai)
  • Received : 2025.11.24
  • Accepted : 2026.04.06
  • Published : 2026.05.30
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Abstract

In this paper, a new clustering optimization framework by combining the cuttlefish optimization algorithm with an internally defined F-measure fitness function has been developed. Unlike most of the existing clustering approaches that use a geometric criterion for clustering, the newly developed clustering framework uses an unsupervised F-measure based on the pairwise consistency of the clusters. In addition to boundedness, monotonicity, consistency, finite-step local convergence, and stochastic convergence with random restarts for the objective function in the optimization process, the theoretical analysis also proves that the process is stable for optimizing this non-convex, combinatorial problem. The experimental results of ten UCI benchmark data sets show that the cuttlefish optimization clustering framework significantly outperforms the widely used k-means algorithm and obtains comparable performance with other evolutionary optimization methods, including PSO, DE, and GA. On more complex and high-dimensional data sets, the proposed framework outperforms other approaches, demonstrating its superior capacity for real-world applications. Using an internally defined F-measure as the optimization criterion makes the cuttlefish optimization framework particularly suitable for exploratory clustering of complex data sets.

Keywords

Acknowledgement

The authors thank Vellore Institute of Technology Chennai, for providing the research facilities used in this work.

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