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.

翻译：灰狼优化算法（Grey Wolf Optimizer, GWO）是一种受灰狼社会领导层级和捕猎机制启发的新型元启发式算法。该算法以其参数设置简单、收敛速度快和优化能力强的特点而广为人知。然而，原始GWO的基础优化机制存在两个显著设计缺陷。问题（1）：算法在生成狼群下一代位置时未能继承上一代的精英位置，可能导致次优解。问题（2）：种群位置基于三只头狼（alpha、beta、delta）的中心位置更新，缺乏局部搜索与全局搜索的平衡机制。针对上述问题，本文提出一种融合精英继承机制与平衡搜索机制的增强型灰狼优化算法（EBGWO），以提高位置更新的有效性和收敛解的质量。采用IEEE CEC 2014基准函数测试集及一系列仿真实验评估所提算法的性能。仿真实验包括EBGWO与三种GWO变体、原始GWO以及两种知名元启发式算法的对比研究。实验结果表明，所提出的EBGWO算法在精度和收敛速度方面均优于其他元启发式算法。通过三个工程优化问题验证其处理实际问题的能力，结果显示所提出的EBGWO算法优于多种主流算法。