Text Document Clustering (TDC) is a challenging optimization problem in unsupervised machine learning and text mining. The Salp Swarm Algorithm (SSA) has been found to be effective in solving complex optimization problems. However, the SSA’s exploitation phase requires improvement to solve the TDC problem effectively. In this paper, we propose a new approach, known as the Bare-Bones Salp Swarm Algorithm (BBSSA), which leverages Gaussian search equations, inverse hyperbolic cosine control strategies, and greedy selection techniques to create new individuals and guide the population towards solving the TDC problem. We evaluated the performance of the BBSSA on six benchmark datasets from the text clustering domain and six scientific papers datasets extracted from the top eight UAE universities. The experimental results demonstrate that the BBSSA algorithm outperforms traditional SSA and nine other optimization algorithms. Furthermore, the BBSSA algorithm achieves better results than the five traditional clustering techniques.
Algorithm design and analysis, Bare Bones, Clustering algorithms, Convergence, Feature extraction, Global Optimization, Greedy Selection Strategy, Optimization, Particle swarm optimization, Salp Swarm Algorithm, Search problems, Task analysis, Text Document clustering, Text mining
M. A. Al-Betar, A. K. Abasi, G. Al-Naymat, K. Arshad and S. N. Makhadmeh, "Bare-Bones Based Salp Swarm Algorithm for Text Document Clustering," in IEEE Access, vol. 11, pp. 100010-100028, 2023, doi: 10.1109/ACCESS.2023.3314589