Mendeley
TY  _  JOUR
ID  -  13990918251157
TI  -  A New Data Clustering Method Using 4-Gray Wolf Algorithm
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Ajami Bakhtiarvand Laleh
AU  -  Beheshti Zahra
AD  -  دانشگاه آزاد اسلامی واحد نجف آباد
AD  -   دانشکده مهندسی کامپیوتر، واحد نجف¬آباد، دانشگاه آزاد اسلامی، نجف¬آباد، ايران
Y1  -  2022
PY  -  2022
VL  -  4
IS  -  
SP  -  261
EP  -  274
KW  -  Data mining
KW  -   data clustering
KW  -   meta-heuristic algorithm
KW  -   gray wolf optimization (GWO) algorithm
KW  -   4-gray wolf optimization (4GWO) algorithm
KW  -   F-measure
DO  -  
N2  -  Nowadays, clustering methods have received much attention because the volume and variety of data are increasing considerably.The main problem of classical clustering methods is that they easily fall into local optima. Meta-heuristic algorithms have shown good results in data clustering. They can search the problem space to find appropriate cluster centers. One of these algorithms is gray optimization wolf (GWO) algorithm. The GWO algorithm shows a good exploitation and obtains good solutions in some problems, but its disadvantage is poor exploration. As a result, the algorithm converges to local optima in some problems. In this study, an improved version of gray optimization wolf (GWO) algorithm called 4-gray wolf optimization (4GWO) algorithm is proposed for data clustering. In 4GWO, the exploration capability of GWO is improved, using the best position of the fourth group of wolves called scout omega wolves. The movement of each wolf is calculated based on its score. The better score is closer to the best solution and vice versa. The performance of 4GWO algorithm for the data clustering (4GWO-C) is compared with GWO, particle swarm optimization (PSO), artificial bee colony (ABC), symbiotic organisms search (SOS) and salp swarm algorithm (SSA) on fourteen datasets. Also, the efficiency of 4GWO-C is compared with several various GWO algorithms on these datasets. The results show a significant improvement of the proposed algorithm compared with other algorithms. Also, EGWO as an Improved GWO has the second rank among the different versions of GWO algorithms. The average of F-measure obtained by 4GWO-C is 82.172%; while, PSO-C as the second best algorithm provides 78.284% on all datasets.
UR  - rimag.ir/en/Article/29068
L1  - rimag.ir/en/Article/Download/29068
ER  -  
