Open AccessImproved grey wolf algorithm for optimization problems
Author(s) -
Hafiz Maaz Asgher,
Yana Mazwin Mohmad Hassim,
Rozaida Ghazali,
Muhammad Aamir
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v22.i3.pp1573-1579
Subject(s) - benchmark (surveying) , optimization algorithm , mathematical optimization , meta heuristic , heuristic , computer science , algorithm , optimization problem , relation (database) , whale , mathematics , data mining , geography , geodesy , fishery , biology
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.