Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms inspired from the social collaboration and team hunting activities of grey wolves in nature. This algorithm benefits from stochastic operators, but it is still prone to stagnation in local optima and premature convergence when solving problems with a large number of variables (e.g., clustering problems). To alleviate this shortcoming, the GWO algorithm is hybridized with the well-known tabu search (TS). To investigate the performance of the proposed hybrid GWO and TS (GWOTS), it is compared with well-regarded metaheuristics on various clustering datasets. The comprehensive experiments and analysis verify that the proposed GWOTS shows an improved performance compared to GWO and can be utilized for clustering applications.

中文翻译：

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使用一种新颖的基于位置信息的灰太狼启发式聚类方法进行聚类分析

灰狼优化器（GWO）是近来流行的元启发式算法之一，其灵感来自自然界中灰狼的社交协作和团队狩猎活动。该算法得益于随机算子，但是在求解具有大量变量的问题（例如，聚类问题）时，仍然倾向于局部最优和过早收敛。为了缓解此缺点，将GWO算法与众所周知的禁忌搜索（TS）进行了混合。为了研究所提出的混合GWO和TS（GWOTS）的性能，将其与各种聚类数据集上备受推崇的元启发法进行了比较。全面的实验和分析证明，与GWO相比，所提出的GWOTS具有更高的性能，可用于集群应用。