BEPO: A novel binary emperor penguin optimizer for automatic feature selection
Knowledge-Based Systems, vol.211, 2021 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 211
- Publication Date: 2021
- Doi Number: 10.1016/j.knosys.2020.106560
- Journal Name: Knowledge-Based Systems
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Computer & Applied Sciences, INSPEC, Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA)
- Keywords: Bio-inspired algorithm, Discrete optimization, Emperor penguin optimizer, Feature selection
- Trakya University Affiliated: Yes
Abstract
Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin's huddling behaviour. However, the original version of the EPO will fix issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm.