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

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