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Multi-objective PSO with Pareto Neighborhood topology for Clustering
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  • Yassmine Soussi ,
  • Nizar Rokbani ,
  • Ali Wali ,
  • Adel Alimi ,
  • Seyedali Mirjalili
Yassmine Soussi
University of Sousse, University of Sousse

Corresponding Author:[email protected]

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Nizar Rokbani
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Adel Alimi
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Seyedali Mirjalili
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In this paper a new technique is integrated to Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, named Pareto Neighborhood (PN) topology, to produce MOPSO-PN algorithm. This technique involves iteratively selecting a set of best solutions from the Pareto-Optimal-Fronts and trying to explore them in order to find better clustering results in the next iteration. MOPSO-PN was then used as a Multi?Objective Clustering Optimization (MOCO) Algorithm, it was tested on various datasets (real-life and artificial datasets). Two scenarios have been used to test the performances of MOPSO-PN for clustering: In the first scenario MOPSO-PN utilizes, as objective functions, two clusters validity index (Silhouette?Index and overall-cluster-deviation), three datasets for test, four algorithms for comparison and the average Minkowski Score as metric for evaluating the final clustering result; In the second scenario MOPSO-PN used, as objectives functions, three clusters validity index (I-index, Con-index and Sym?index), 20 datasets for test, ten algorithms for comparison and the F-Measure as metric for evaluating the final clustering result. In both scenarios, MOPSO-PN provided a competitive clustering results and a correct number of clusters for all datasets.