An enhanced Teaching-Learning-Based Optimization (TLBO) with Grey Wolf
  Optimizer (GWO) for text feature selection and clustering
Mahsa Azarshab Mohammad Fathian Babak Amiri
Text document clustering can play a vital role in organizing and handling the
everincreasing number of text documents. Uninformative and redundant features
included in large text documents reduce the effectiveness of the clustering
algorithm. Feature selection (FS) is a well-known technique for removing these
features. Since FS can be formulated as an optimization problem, various
meta-heuristic algorithms have been employed to solve it.
Teaching-Learning-Based Optimization (TLBO) is a novel meta-heuristic algorithm
that benefits from the low number of parameters and fast convergence. A hybrid
method can simultaneously benefit from the advantages of TLBO and tackle the
possible entrapment in the local optimum. By proposing a hybrid of TLBO, Grey
Wolf Optimizer (GWO), and Genetic Algorithm (GA) operators, this paper suggests
a filter-based FS algorithm (TLBO-GWO). Six benchmark datasets are selected,
and TLBO-GWO is compared with three recently proposed FS algorithms with
similar approaches, the main TLBO and GWO. The comparison is conducted based on
clustering evaluation measures, convergence behavior, and dimension reduction,
and is validated using statistical tests. The results reveal that TLBO-GWO can
significantly enhance the effectiveness of the text clustering technique
(K-means).