A Hybrid Bio-Inspired Tabu Search Clustering Approach

• Dragan Simić ^[1] ; Zorana Banković ^[4] ; Villar, José R. ^[2] ; José Luis Calvo-Rolle ^[3] ; Simić, Svetislav D. ^[1] ; Svetlana Simić ^[1]
  1. [1] University of Novi Sad

     University of Novi Sad

     RS.VO.6.3194359, Serbia

     
  2. [2] Universidad de Oviedo

     Universidad de Oviedo

     Oviedo, España

     
  3. [3] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
  4. [4] Frontiers Media SA (Madrid)

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• Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López , Pablo García Bringas , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2021, ISBN 978-3-030-86271-8, págs. 436-447
• Idioma: inglés
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• Resumen

  • The purpose of a data clustering process is to group a set of objects into multiple classes so that the objects in each class – cluster are similar according to certain rules or criteria, where the definition of similarity can be problem dependent. This paper is focused on a new bio-inspired clustering approach based on a model for combining tabu search algorithm (TS) and firefly algorithm (FF). The proposed hybrid bio-inspired system is tested on two well-known Iris and Wine data sets. Finally, the experimental results are compared with the parallel tabu search clustering algorithm. The proposed bio-inspired TS-FF clustering system shows a significantly better accuracy value for Iris data set.