Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach

  • Ibrahim AljarahEmail author
  • Majdi Mafarja
  • Ali Asghar Heidari
  • Hossam Faris
  • Seyedali Mirjalili
Regular Paper


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.


Optimization Grey wolf optimizer GWO Tabu search Data clustering 



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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  2. 2.Department of Computer ScienceBirzeit UniversityBirzeitPalestine
  3. 3.School of Surveying and Geospatial EngineeringUniversity of TehranTehranIran
  4. 4.Department of Computer Science, School of ComputingNational University of SingaporeSingaporeSingapore
  5. 5.School of Information and Communication TechnologyGriffith UniversityNathan, BrisbaneAustralia

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