A Dynamic Opposite Learning-Assisted Grey Wolf Optimizer

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 algorithm, 23 benchmark functions from CEC2014 were adopted numerical experiments. A total 10 popular algorithms, including GWO, TLBO, PIO, Jaya, CFPSO, CFWPSO, ETLBO, CTLBO, NTLBO DOLJaya used make comparisons algorithm. Results indicate that new model strong robustness adaptability, significant advantage converging optimum, demonstrates DOL greatly improves original GWO