Adaptive Grey wolf Optimization Algorithm with Gaussian Mutation

Grey wolf optimizer is a well-known optimization algorithm and is still being investigated by the researcher to improve its performance for applying in complex optimization problems. This paper introduced an adaptive grey wolf optimization algorithm (AGWO) by providing adequate exploration to the grey wolves during hunting for the prey. The exploration procedure of the original grey wolf algorithm (GWO) dominates up to half of the iterations after the remaining part of iterations dedicated to the exploitation process. This restriction in exploration leads to a lack of population diversity in the GWO. To overcome this problem, the proposed method adaptively switched to the Gaussian mutation stage with a certain probability. To assess performance, the performance of the proposed algorithm is tested with a set of 23 benchmark functions defined in the CEC2005 data suit and compared with other standard optimization algorithms along with GWO. The results reveal that the proposed algorithm exceeds the well-known optimization algorithms, the GWO.