Journal of Bionic Engineering (2023) 20:2973–3007 https://doi.org/10.1007/s42235-023-00400-7

Comprehensive Learning Strategy Enhanced Chaotic Whale Optimization for High?dimensional Feature Selection 

Hanjie Ma1  · Lei Xiao1  · Zhongyi Hu1  · Ali Asghar Heidari1  · Myriam Hadjouni2  · Hela Elmannai3  · Huiling Chen1

Lei Xiao xiaolei@wzu.edu.cn * Zhongyi Hu huzhongyi@wzu.edu.cn * Huiling Chen chenhuiling.jlu@gmail.com Hanjie Ma mhanjie@163.com Ali Asghar Heidari aliasghar68@gmail.com Myriam Hadjouni mfhaojouni@pnu.edu.sa Hela Elmannai hselmannai@pnu.edu.sa 

1 Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China 

2 Department of Computer Sciences, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671 Riyadh, Saudi Arabia 

3 Department of Information Technology, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671 Riyadh, Saudi Arabia

Abstract: Feature selection (FS) is an adequate data pre-processing method that reduces the dimensionality of datasets and is used in bioinformatics, fnance, and medicine. Traditional FS approaches, however, frequently struggle to identify the most important characteristics when dealing with high-dimensional information. To alleviate the imbalance of explore search ability and exploit search ability of the Whale Optimization Algorithm (WOA), we propose an enhanced WOA, namely SCLWOA, that incorporates sine chaos and comprehensive learning (CL) strategies. Among them, the CL mechanism contributes to improving the ability to explore. At the same time, the sine chaos is used to enhance the exploitation capacity and help the optimizer to gain a better initial solution. The hybrid performance of SCLWOA was evaluated comprehensively on IEEE CEC2017 test functions, including its qualitative analysis and comparisons with other optimizers. The results demonstrate that SCLWOA is superior to other algorithms in accuracy and converges faster than others. Besides, the variant of Binary SCLWOA (BSCLWOA) and other binary optimizers obtained by the mapping function was evaluated on 12 UCI data sets. Subsequently, BSCLWOA has proven very competitive in classifcation precision and feature reduction. 

Keywords: Feature selection · Whale Optimization Algorithm · Binary optimizer · Global optimization