Grey Wolf Optimization using Improved mutation oppositional based learning for optimization problems

Grey wolf optimization (GWO) algorithm is a swarm intelligence optimization technique that is developed by Mirjalili to mimic the hunting behavior and leadership hierarchy of grey wolves in nature. Oppositional-based learning (OBL) is a new concept, which has attracted many research efforts in the last decade, some optimization methods have already used the concept of OBL to improve their performance. This paper presents an efficient algorithm, namely, Improved Mutation Oppositional-based learning GWO (IMOGWO) based on Quasi Oppositional-based learning (QOBL) and Topological Oppositional-based learning (TOBL) with some parameter adjustments to improve exploration and exploitation. The experiments were executed 28 widely used benchmark test functions with various features. The results reveal that the proposed algorithm improves the exploration capability and is effective, ranking first in average value compared to the comparison algorithm.