MOPSOPN journal paper.pdf (529.27 kB)
Multi-objective PSO with Pareto Neighborhood topology for Clustering
preprint
posted on 2021-06-04, 20:52 authored by Yassmine SoussiYassmine Soussi, Nizar Rokbani, Seyedali MirjaliliSeyedali Mirjalili, Ali Wali, Adel AlimiAdel AlimiIn 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.
History
Email Address of Submitting Author
yassmine.soussi@ieee.orgORCID of Submitting Author
0000-0002-5450-8147Submitting Author's Institution
University of Sousse, ISITCom, 4011, Sousse, TunisiaSubmitting Author's Country
- Tunisia