| Popis: |
Metaheuristic algorithms are more popular due to their ability to solve generic problems. Selection and tuning of their algorithm specific control parameters are the key factors for their success. Grey Wolf Optimizer, an algorithm having less dependency on their control parameters and influenced by the hunting approach and social dominant hierarchy of grey wolves is considered here to solve global optimization problems. An improved GWO is proposed to achieve a perfect ratio of exploitation and exploration phases and gives more weight to the most suitable wolves to find the new position of grey wolves during the stated iterations. Simulation results of standard benchmark test functions convey the stability, efficiency, and effectiveness of iGWO compared with GWO, and other standard algorithms. |
