  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.
