An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection
Zhiqing Chen,
Ping Xuan,
Ali Asghar Heidari,
Lei Liu,
Chengwen Wu,
Huiling Chen,
José Escorcia-Gutierrez,
Romany F. Mansour
Affiliations
Zhiqing Chen
School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou 325035, China
Ping Xuan
Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China; Corresponding author
Ali Asghar Heidari
Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
Lei Liu
College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China
Chengwen Wu
Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China; Corresponding author
Huiling Chen
Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China; Corresponding author
José Escorcia-Gutierrez
Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla 080002, Colombia
Romany F. Mansour
Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
Summary: The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.
