Q-learning improved golden jackal optimization algorithm and its application to reliability optimization of hydraulic system

Abstract To endow the prey with intelligent movement behavior and improve the performance of Golden Jackal Optimization (GJO), a Q-learning Improved Gold Jackal Optimization (QIGJO) algorithm is proposed. This paper introduces five update mechanisms and proposes double-population Q-learning collabor...

Descripció completa

Dades bibliogràfiques

Autors principals:	Dongning Chen, Haowen Wang, Dongbo Hu, Qinggui Xian, Bingyu Wu
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2024-10-01
Col·lecció:	Scientific Reports
Matèries:	Golden jackal optimization Reliability optimization Q-Learning Global optimization
Accés en línia:	https://doi.org/10.1038/s41598-024-75374-5
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!