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

TL;DR

This paper tackles two core limitations of the Grey Wolf Optimizer: lack of elite inheritance across iterations and an imbalanced exploration-exploitation dynamic from centralized updating. It introduces the Enhanced Balance Grey Wolf Optimizer (EBGWO), featuring an Elite Inheritance Mechanism (EIM) and a Balance Search Mechanism (BSM), including an Elite Archive and a ST-based two-stage update to expand search diversity while preserving exploitation. The authors analyze computational complexity and demonstrate, via the IEEE CEC 2014 benchmark and engineering design problems, that EBGWO achieves superior convergence speed and accuracy compared with GWO variants and other meta-heuristics, while better avoiding local optima. The results suggest that the dual-mechanism approach provides robust performance for real-world engineering optimization and offers a foundation for future extensions to large-scale and multi-objective problems.

Abstract

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.

Figures (16)

• Figure 1: Flow chart of the Grey Wolf Optimization algorithm
• Figure 2: The elite inheritance mechanism in EBGWO
• Figure 3: The optimization process of two iterations ($i^{th}$ and $(i+1)^{th}$) in GWO
• Figure 4: Balance search mechanism of EBGWO
• Figure 5: Flow chart of EBGWO