A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm

• Published in: PloS one Vol. 19; no. 1; p. e0295579
• Main Authors: Liu, Guangwei, Guo, Zhiqing, Liu, Wei, Jiang, Feng, Fu, Ensan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.01.2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Animal behavior; Animal populations; Benchmarks; Biology and Life Sciences; Canis aureus; Classification; Computer and Information Sciences; Computer science; Ecology and Environmental Sciences; Feature selection; Females; Health aspects; Innovations; Interpolation; Mathematical optimization; Methods; Optimization; Optimization algorithms; Physical Sciences; Research and Analysis Methods; Social Sciences; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0295579

This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. In Case 2, GJO-GWO was utilized to tackle ten feature selection problems. Experimental results consistently demonstrate that under identical experimental conditions, whether solving complex benchmark functions or addressing feature selection problems, GJO-GWO exhibits smaller means, lower standard deviations, higher classification accuracy, and reduced execution times. These findings affirm the superior optimization performance, classification accuracy, and stability of the GJO-GWO algorithm.