A better exploration strategy in Grey Wolf Optimizer

The Grey Wolf Optimizer (GWO) is a recently developed population-based meta-heuristics algorithm that mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Although, GWO has shown very good results on several real-life applications but still it suffers from some issues like, the low exploration and slow convergence rate. Therefore in this paper, an improved grey wolf optimizer is proposed to modify the exploration as well as exploitation abilities of the classical GWO. This improvement is performed by using the explorative equation and opposition-based learning (OBL). The validation of the proposed modification is done on a set of 23 standard benchmark test problems using statistical, diversity and convergence analysis. The experimental results on test problems confirm that the efficiency of the proposed algorithm is better than other considered metaheuristic algorithms.