Solution of Test Problems with Grey Wolf Optimization Algorithm and Comparison with Particle Swarm Optimization

Authors : Alper KÖYBAŞI, İrfan YAZİCİ

Pages : 1252-1264

Doi:10.16984/saufenbilder.788681

View : 22 | Download : 7

Publication Date : 2020-12-01

Article Type : Research Paper

Abstract :In this study, Grey Wolf Optimization insert ignore into journalissuearticles values(GWO);, which is a new method with swarm intelligence is compared with another metaheuristic optimization method, Particle Swarm Optimization insert ignore into journalissuearticles values(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