Grey wolf optimization (GWO) is a widely used meta-heuristic method. It has limited searching potential when solving the majority of function optimization problems. This paper proposes a new variant of GWO, named SCGWO, which combines GWO with an improved spread strategy and a chaotic local search (CLS) mechanism to overcome these performance limitations. In detail, a spread strategy is introduced into the basic GWO to change the search agent's ability to avoid the local optima, the global exploration capability, and the individual movement's randomness. Then, a CLS mechanism is adopted to accelerate the convergence rate of the evolving agents. This method's effectiveness is illustrated by comparing the proposed SCGWO method with various algorithms, including seven GWO variants and eight well-known state-of-the-art algorithms on a comprehensive set of benchmark functions with the type of unimodal, multimodal, and composition functions. The experimental results confirmed that the established SCGWO algorithm has apparent advantages in processing unimodal, multimodal, and composition functions. Additionally, the proposed algorithm was utilized for finding the approximate optimal feature subset when applied to the feature selection problems on a set of 32 real-world data sets from the UCI machine learning repository. The results show that the binary variant also reveals a very competitive performance in dealing with feature selection. Our findings and analysis suggest that the proposed method can be a very suitable tool for realizing the optimal solutions to global optimization and wrapper-based feature selection tasks.

中文翻译：

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混沌扩散限制聚合增强灰狼优化器：洞察、分析、二值化和特征选择

灰狼优化（GWO）是一种广泛使用的元启发式方法。在解决大多数函数优化问题时，它的搜索潜力有限。本文提出了一种新的 GWO 变体，称为 SCGWO，它将 GWO 与改进的传播策略和混沌局部搜索 (CLS) 机制相结合，以克服这些性能限制。具体而言，在基本 GWO 中引入了传播策略，以改变搜索智能体避免局部最优的能力、全局探索能力和个体运动的随机性。然后，采用 CLS 机制来加快进化代理的收敛速度。通过将所提出的 SCGWO 方法与各种算法进行比较来说明该方法的有效性，包括 7 个 GWO 变体和 8 个著名的最先进的算法，这些算法针对具有单峰、多峰和组合函数类型的全面基准函数集。实验结果证实，所建立的SCGWO算法在处理单峰、多峰和复合函数方面具有明显的优势。此外，当应用于来自 UCI 机器学习存储库的一组 32 个真实世界数据集的特征选择问题时，所提出的算法被用于寻找近似最优特征子集。结果表明，二元变体在处理特征选择方面也表现出非常有竞争力的性能。