The Grey Wolf Optimizer (GWO) is recognized as a novel meta-heuristic
algorithm inspired by the social leadership hierarchy and hunting mechanism of
grey wolves. It is well-known for its simple parameter setting, fast
convergence speed, and strong optimization capability. In the original GWO,
there are two significant design flaws in its fundamental optimization
mechanisms. Problem (1): the algorithm fails to inherit from elite positions
from the last iteration when generating the next positions of the wolf
population, potentially leading to suboptimal solutions. Problem (2): the
positions of the population are updated based on the central position of the
three leading wolves (alpha, beta, delta), without a balanced mechanism between
local and global search. To tackle these problems, an enhanced Grey Wolf
Optimizer with Elite Inheritance Mechanism and Balance Search Mechanism, named
as EBGWO, is proposed to improve the effectiveness of the position updating and
the quality of the convergence solutions. The IEEE CEC 2014 benchmark functions
suite and a series of simulation tests are employed to evaluate the performance
of the proposed algorithm. The simulation tests involve a comparative study
between EBGWO, three GWO variants, GWO and two well-known meta-heuristic
algorithms. The experimental results demonstrate that the proposed EBGWO
algorithm outperforms other meta-heuristic algorithms in both accuracy and
convergence speed. Three engineering optimization problems are adopted to prove
its capability in processing real-world problems. The results indicate that the
proposed EBGWO outperforms several popular algorithms.

通过引入精英继承机制和平衡搜索机制，提出了一种改进的 EBGWO 算法来解决原始 GWO 算法中的设计缺陷，实验证明该算法在准确性和收敛速度方面优于其他元启发式算法。

具有精英继承和平衡搜索机制的增强灰狼优化器

An Enhanced Grey Wolf Optimizer with Elite Inheritance and Balance  Search Mechanisms

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。