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).

利用 Teaching-Learning-Based Optimization、Grey Wolf Optimizer 和 Genetic Algorithm 等算法的 TLBO-GWO 过滤特征选择算法可显著提升文本聚类技术 (K-means) 的有效性。

基于灰狼优化器的增强教学学习优化模型在文本特征选择和聚类中的应用

An enhanced Teaching-Learning-Based Optimization (TLBO) with Grey Wolf  Optimizer (GWO) for text feature selection and clustering

This paper introduces a new path planning algorithm for unmanned aerial
vehicles (UAVs) based on the teaching-learning-based optimization (TLBO)
technique. We first define an objective function that incorporates requirements
on the path length and constraints on the movement and safe operation of UAVs
to convert the path planning into an optimization problem. The optimization
algorithm named Multi-subject TLBO is then proposed to minimize the formulated
objective function. The algorithm is developed based on TLBO but enhanced with
new operations including mutation, elite selection and multi-subject training
to improve the solution quality and speed up the convergence rate. Comparison
with state-of-the-art algorithms and experiments with real UAVs have been
conducted to evaluate the performance of the proposed algorithm. The results
confirm its validity and effectiveness in generating optimal, collision-free
and flyable paths for UAVs in complex operating environments.

本论文提出了一种基于 TLBO 技术的无人机航路规划算法，通过定义目标函数并使用 Multi-subject TLBO 算法进行优化，可以生成复杂环境下优化的、无碰撞、可飞行的路径规划方案。