On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

Abstract

Grey Wolf Optimizer (GWO) is a populationbased evolutionary algorithm inspired by the hunting behaviour of grey wolves. GWO, in its basic form, is a real coded algorithm, therefore, it needs modifications to deal with binary optimization problems. In this paper, we review previous works on binarization of GWO, and classify them with respect to their encoding scheme, updating strategy, and transfer function. Then, we propose a novel binary GWO algorithm (named Set-GWO), which is based on set encoding and uses set operations in its updating strategy. Experimental results on different real-world combinatorial optimization problems and different datasets, show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions, running time, and scalability.