Enhanced leadership-inspired grey wolf optimizer for global optimization problems

Abstract

Grey wolf optimizer (GWO) is a recently developed population-based algorithm in the area of nature-inspired optimization. The leading hunters in GWO are responsible for exploring the new promising regions of the search space. However, in some circumstances, the classical GWO suffers from the problem of premature convergence due to the stagnation at sub-optimal solutions. The insufficient guidance of search in GWO leads to slow convergence. Therefore, to alleviate from all the above issues, an improved leadership-based GWO called GLF–GWO is introduced in the present paper. In GLF–GWO, the leaders are updated through Levy-flight search mechanism. The proposed GLF–GWO algorithm enhances the search efficiency of leading hunters in GWO and provides better guidance to accelerate the search process of GWO. In the GLF–GWO algorithm, the greedy selection is introduced to avoid their divergence from discovered promising areas of the search space. To validate the efficiency of the GLF–GWO, the standard benchmark suite IEEE CEC 2014 and IEEE CEC 2006 are taken. The proposed GLF–GWO algorithm is also employed to solve some real-engineering problems. Experimental results reveal that the proposed GLF–GWO algorithms significantly improve the performance of the classical version of GWO.