Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by Abdelaziz Bouroumi
Total Records ( 5 ) for Abdelaziz Bouroumi
  Abdelaziz El Moujahid , Khalid Jebari , Abdelaziz Bouroumi and Aziz Ettouhami
  Tournament selection is a popular selection scheme commonly used in genetic algorithms. This method depends largely on the size of the tournament which influences the quality of the final solution. In this study, a new technique is used to dynamically adjust the tournament size using the fuzzy c-means algorithm. The efficiency of the proposed technique is shown by using several benchmark test functions.
  Khalid Jebari , Abdelaziz Bouroumi and Aziz Ettouhami
  The study deals with the efficiency of the parallel computation of the Travelling Salesman Problem (TSP) using the genetic algorithms and an unsupervised fuzzy clustering. First, the cities are classified by a clustering algorithms. Second, each class of cities is considered as a sub-tour TSP problem. A parallel genetic algorithms is used for solving the sub tour TSP problem. The main aim by creating the parallel algorithm is to accelerate the execution time of solving TSP. A connection method is proposed to connect the sub-tours into a global tour of whole cities. Furthermore, this global tour is resolved by genetic algorithms for cluster centers and a heuristic scheme. Experimental results, on Master Slave architecture with different TSP problemes show the efficacy of the proposed algorithm in parallelism exploitation.
  Hanane Benrachid , Rkia Fajr and Abdelaziz Bouroumi
  Researchers propose a new algorithm for detecting homogeneous clusters within sets of unlabeled objects represented by numerical data of the form X = {x1, x2,..., xn} . By quickly exploring the available data using an inter-objects similarity measure plus an ambiguity measure of individual objects, this algorithm provides the number of clusters present in X, plus a set of optimized prototypes V = {v1, v2,..., vn} where each prototype characterizes one of the c detected clusters. The performance of the algorithm is illustrated by typical examples of simulation results obtained for different real test data.
  Amina Dik , Khalid Jebari , Abdelaziz Bouroumi and Aziz Ettouhami
  This study presents a new approach for partitioning data sets affected by outliers. The proposed scheme consists of two main stages. The first stage is a preprocessing technique that aims to detect data value to be outliers by introducing the notion of object’s proximity degree. The second stage is a new procedure based on the Fuzzy C-Means (FCM) algorithm and the concept of outliers clusters. It consists to introduce clusters for outliers in addition to regular clusters. The proposed algorithm initializes their centers by the detected possible outliers. Final and accurate decision is made about these possible outliers during the process. The performance of this approach is also illustrated through real and artificial examples.
  Khalid Jebari , Amina Dik , Abdelaziz Bouroumi and Aziz Ettouhami
  Genetic algorithms are optimization and search methods based on the principles of Darwinian evolution and genetics that try to provide the optimal solution of a problem. They evolve a population of candidate solutions to the problem, using mutation, crossover and selection operators. Based on the diversity and the efficiency of four well known crossover operators, this study presents a novel operator called Combined Crossover Operator (CCO). The comparison with those four crossover operators shows that the results obtained by the CCO are promising.
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility