SMCFO: a novel cuttlefish optimization algorithm enhanced by simplex method for data clustering.

Introduction: In unsupervised learning, data clustering is essential. However, many current algorithms have issues like early convergence, inadequate local search capabilities, and trouble processing complicated or unbalanced input. Established methods like Kmeans are still widely used because of their ease of use; however, they struggle with non-spherical cluster shapes, which are sensitive to initialization, and suffer in highdimensional space. As a substitute, metaheuristic algorithms have surfaced as possible options, providing powerful global search ability. The Cuttlefish Optimization Algorithm (CFO) shows promise in clustering applications but suffers from premature convergence and poor local optimization capability. Methods: This paper introduces a new clustering method based on the Cuttlefish Optimization Algorithm (CFO), which improves upon the Nelder-Mead simplex method known as SMCFO. The method partitions the population into four subgroups with specific update strategies. One subgroup uses the Nelder-Mead method to improve the quality of solutions, while the others attempt to maintain exploration and exploitation equilibrium. This study compares the performance of the suggested SMCFO algorithm with four established clustering algorithms: CFO, PSO, SSO, and SMSHO. The evaluation used 14 datasets, which include two artificial datasets and 12 benchmark datasets sourced from the UCI Machine Learning Repository. Results and discussion: The proposed SMCFO algorithm consistently outperformed competing methods across all datasets, achieving higher clustering accuracy, faster convergence, and improved stability. The robustness of these outcomes was further confirmed through nonparametric statistical tests, which demonstrated that the performance improvements of SMCFO were statistically significant and not due to chance. The results confirm that the simplex-enhanced design boosts local exploitation and stabilizes convergence, which underlies SMCFO's superior performance compared to baseline methods.