TY  - JOUR
TI  - Solution of Test Problems with Grey Wolf Optimization Algorithm and Comparison with Particle Swarm Optimization
AB  - In this study, Grey Wolf Optimization (GWO), which is a new method with swarm intelligence is compared with another metaheuristic optimization method, Particle Swarm Optimization (PSO), using optimization benchmark functions. Simulation studies on test functions are presented as a table by obtaining mean, standard deviation, best and worst values. In addition, the effects of population and iteration number change on the GWO algorithm are presented in separate tables. The GWO algorithm has establish a good balance between exploration and exploitation. Simulation studies have shown that GWO has better convergence performance and optimization accuracy.Keywords: Grey Wolf Optimization, Metaheuristic Optimization, Particle Swarm Optimization
AU  - YAZICI, IRFAN
AU  - Köybaşı, Alper
DO  - 10.16984/saufenbilder.788681
PY  - 2020
JO  - Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi
VL  - 24
IS  - 6
SN  - 1301-4048
SP  - 1252
EP  - 1264
DB  - TRDizin
UR  - http://search/yayin/detay/471882
ER  -