This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, enc...

In this work, a novel binary version of the grey wolf optimization (GWO) is proposed and used to select optimal feature subset for classification purposes. Grey wolf optimizer (GWO) is one of the latest bioinspired optimization techniques, which simulate the hunting process of grey wolves in nature. The binary version introduced here is performed using two different approaches. In the first app...

The grey wolf optimization (GWO) algorithm is widely utilized in many global applications. In this paper, a dynamic opposite learning-assisted optimizer (DOLGWO) was proposed to improve the search ability. Herein, learning (DOL) strategy adopted, which has an asymmetric space and can adjust with random point enhance exploitation exploration capabilities. To validate performance of DOLGWO algori...