Grey wolf optimizer (GWO) is an effective meta-heuristic technique which has been widely utilized to solve numerical optimization as well as real-world applications. However, GWO has some shortcomings, i.e., low solution accuracy, slow convergence, and easy stagnation at local optima in solving complex problems. To tackle these shortcomings, an enhanced GWO called EGWO is developed in this study. This enhancement is achieved by embedding three novel strategies into the basic GWO to improve its performance. Firstly, a new transition mechanism is designed instead of the original strategy to obtain a good transition from the exploration to exploitation. Secondly, the cuckoo search algorithm is... More >