Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to sea...

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Detalles Bibliográficos
Autores principales: Xing, Jie, Zhao, Hanli, Chen, Huiling, Deng, Ruoxi, Xiao, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707266/
https://www.ncbi.nlm.nih.gov/pubmed/36466725
http://dx.doi.org/10.1007/s42235-022-00297-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707266/
https://www.ncbi.nlm.nih.gov/pubmed/36466725
http://dx.doi.org/10.1007/s42235-022-00297-8

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