Mutation-driven grey wolf optimizer with modified search mechanism

The Grey wolf optimizer (GWO) is a recently introduced popular swarm-intelligence-based metaheuristic algorithm, compared to other algorithms, it has shown competitive performance. Despite its popularity, the conventional GWO suffers from slow convergence rate and tendency to stuck in local optima. Therefore, there is a chance of improvement in the search mechanism of the GWO through different operators. To improve the performance of the GWO, this paper proposes a new variant of the GWO called Mutation-driven Modified Grey wolf optimizer and denoted by MDM-GWO. The MDM-GWO combines a new update search mechanism, modified control parameter, mutation-driven scheme, and greedy approach of selection in the search procedure of the GWO. The performance of the proposed MDM-GWO is evaluated on 23 well-known standard benchmark problems of wide varieties of complexities and four real-world engineering design problems. The numerical results, statistical tests, convergence, and diversity curves, and comparisons among several algorithms show the superiority of the proposed MDM-GWO.