Improved grey wolf algorithm for optimization problems

Hafiz Maaz Asgher (Universiti Tun Hussein Onn Malaysia (UTHM))
Yana Mazwin Mohmad Hassim (Universiti Tun Hussein Onn Malaysia (UTHM))
Rozaida Ghazali (Universiti Tun Hussein Onn Malaysia (UTHM))
Muhammad Aamir (University of Derby)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 22, No 3: June 2021

The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms. However, due to its poor exploration capability, it has imbalance relation between exploration and exploitation. Therefore, in this research work, the poor exploration part of GWO was improved through hybrid with whale optimization algorithm (WOA) exploration. The proposed grey wolf whale optimization algorithm (GWWOA) was evaluated on five unimodal and five multimodal benchmark functions. The results shows that GWWOA offered better exploration ability and able to solve the optimization problem and give better solution in search space. Additionally, GWWOA results were well balanced and gave the most optimal in search space as compare to the standard GWO and WOA algorithms.