Multi-strategy ensemble grey wolf optimizer and its application to feature selection

• Published in: Applied soft computing Vol. 76; pp. 16 - 30
• Main Authors: Tu, Qiang, Chen, Xuechen, Liu, Xingcheng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2019
• Subjects: Feature selection; Function optimization; Grey wolf optimizer; Intelligent simulation; Multi-strategy ensemble; Feature selection; Grey wolf optimizer; Function optimization; Intelligent simulation; Multi-strategy ensemble
• DOI: 10.1016/j.asoc.2018.11.047
• ISSN: 1568-4946

To overcome the limitation of single search strategy of grey wolf optimizer (GWO) in solving various function optimization problems, we propose a multi-strategy ensemble GWO (MEGWO) in this paper. The proposed MEGWO incorporates three different search strategies to update the solutions. Firstly, the enhanced global-best lead strategy can improve the local search ability of GWO by fully exploiting the search space around the current best solution. Secondly, the adaptable cooperative strategy embeds one-dimensional update operation into the framework of GWO to provide a higher population diversity and promote the global search ability. Thirdly, the disperse foraging strategy forces a part of search agents to explore a promising area based on a self-adjusting parameter, which contributes to the balance between the exploitation and exploration. We conducted numerical experiments based on various functions form CEC2014. The obtained results are compared with other three modified GWO and seven state-of-the-art algorithms. Furthermore, feature selection is employed to investigate the effectiveness of MEGWO on real-world applications. The experimental results show that the proposed algorithm which integrate multiple improved search strategies, outperforms other variants of GWO and other algorithms in terms of accuracy and convergence speed. It is validated that MEGWO is an efficient and reliable algorithm not only for optimization of functions with different characteristics but also for real-world optimization problems. •A multi-strategy ensemble GWO is proposed to boost the precision and efficiency of the original GWO.•A parameter self-adjusting strategy is utilized to balance the exploitation and exploration of the proposed MEGWO.•Wilcoxons signed-rank test and performance profile are used to investigate the significance of the MEGWO.•Feature selection is employed to evaluate the effectiveness of MEGWO on real-world applications.