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

Abstract

Grey Wolf Optimizer (GWO) is a nature-inspired swarm intelligence algorithm that mimics the hunting behavior of grey wolves. GWO, in its basic form, is a real coded algorithm that needs modifications to deal with binary optimization problems. In this paper, previous work on the binarization of GWO are reviewed, and are classified with respect to their encoding scheme, updating strategy, and transfer function. Then, we propose a novel binary GWO algorithm (named SetGWO), which is based on set encoding and uses set operations in its updating strategy. The proposed algorithm uses a completely different encoding scheme that eliminates the need for the transfer function and boundary checking, and also uses lower-dimensional agents; therefore, decreases the running time. Also, by using an exclusive exploration set for each agent, defining a different distance measure and an encircling strategy in discrete spaces, the quality of solutions has been improved. Experimental results on different real-world combinatorial optimization problems and datasets show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions, running time, and scalability.