SMCFO: A Novel Cuttlefish Optimization Algorithm enhanced by Simplex Method for Data Clustering

Kalpanarani KHannah Grace G^*

• Vellore Institute of Technology - Chennai Campus, Chennai, India

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 K-Means 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 high-dimensional 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. 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. SMCFO outperformed all datasets with higher clustering accuracy, fast convergence, and improved stability. Additionally, Wilcoxon rank-sum tests confirmed these outcomes, ensuring that the gains realized were not by chance and were statistically significant.