Publications Scientifiques

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Author: search.filters.author.Berrichi, A.

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    Gabriel graph-based connectivity and density for internal validity of clustering
    (Springer link, 2020) Boudane, Fatima; Berrichi, A.
    Clustering has an important role in data mining field. However, there is a large variety of clustering algorithms and each could generate quite different results depending on input parameters. In the research literature, several cluster validity indices have been proposed to evaluate clustering results and find the partition that best fits the input dataset. However, these validity indices may fail to achieve satisfactory results, especially in case of clusters with arbitrary shapes. In this paper, we propose a new cluster validity index for density-based, arbitrarily shaped clusters. Our new index is based on the density and connectivity relations extracted among the data points, based on the proximity graph, Gabriel graph. The incorporation of the connectivity and density relations allows achieving the best clustering results in the case of clusters with any shape, size or density. The experimental results on synthetic and real datasets, using the well-known neighborhood-based clustering (NBC) algorithm and the DBSCAN (density-based spatial clustering of applications with noise) algorithm, illustrate the superiority of the proposed index over some classical and recent indices and show its effectiveness for the evaluation of clustering algorithms and the selection of their appropriate parameters
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    Bi-objective optimization algorithms for joint production and maintenance scheduling : application to the parallel machine problem
    (Springer, 2009) Berrichi, A.; Amodeo, L.; Yalaoui, F.; Châtelet, E.; Mezghiche, Mohamed
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    Bi-objective ant colony optimization approach to optimize production and maintenance scheduling
    (Elsevier, 2010) Mezghiche, Mohamed; Amodeo, L.; Yalaoui, F.; Berrichi, A.
    This paper presents an algorithm based on Ant Colony Optimization paradigm to solve the joint production and maintenance scheduling problem .This approach is developed to deal with the model previously proposed in [3] for the parallel machine case. This model is formulated according to a bi- objective approach to find trade-off solutions between both objectives of production and maintenance. Reliability models are used to take in to account the maintenance aspect. To improve the quality of solutions found in our previous study, an algorithm based on Multi-Objective Ant Colony Optimization (MOACO) approach is developed. The goal is to simultaneously determine the best assignment of production tasks to machines as well as preventive maintenance (PM) periods of the production system, satisfying at best both objectives of production and maintenance. The experimental results show that the proposed method out performs two well-known Multi-Objective Genetic Algorithms (MOGAs): SPEA 2and NSGAII