Novel grey wolf optimization based on modified differential evolution for numerical function optimization

Abstract

Grey wolf optimization algorithm (GWO) is a new swarm intelligence optimization algorithm proposed in recent years. Because of few control parameters and easy implementation, GWO is widely used in many fields. Compared with other common swarm optimization algorithms, it is more suitable for the global optimization problems. Nevertheless, the algorithm still has the shortcoming of low accuracy and slow convergence speed. In this paper, a novel hybrid optimization algorithm named MDE-GWO is proposed. Firstly, a JADE with opposition-based learning strategy algorithm (MDE) is embedded in GWO to enhance the ability of avoiding a local optimum. Notably, by introducing the opposition-based learning strategy, the search ability of the improved algorithm is increased greatly. Additionally, in order to balance the global and local search capabilities and speed up the convergence of the GWO algorithm, a leader moving-rate strategy is put forward. 28 typical benchmark functions are utilized to test the performance of the improved algorithm. The experimental results show that MDE-GWO has stronger advantages in search accuracy, stability and convergence speed in most cases.