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

被引：3

作者：

Chen, Dongning ^{[1
]}

Wang, Haowen ^{[1
]}

Hu, Dongbo ^{[1
]}

Xian, Qinggui ^{[1
]}

Wu, Bingyu ^{[1
]}

机构：

[1] Yanshan Univ, Sch Mech Engn, Qinhuangdao 066004, Peoples R China

来源：

SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Golden jackal optimization; Reliability optimization; Q-Learning; Global optimization; SALP SWARM ALGORITHM; DESIGN;

D O I：

10.1038/s41598-024-75374-5

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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 collaborative mechanism to select appropriate update mechanisms to improve GJO performance. Additionally, a new convergence factor is incorporated to enhance convergence capability of GJO. QIGJO demonstrates excellent performance across 23 benchmark functions, CEC2022, and three classical engineering design problems, indicating high convergence accuracy and significantly enhanced global exploration capability. The reliability optimization model of the hydraulic system for concrete pump trucks was established based on a Continuous-time Multi-dimensional T-S dynamic Fault Tree (CM-TSdFT), considering the two-dimensional factors of operating time and number of impacts. Utilizing QIGJO to optimize this model yielded excellent results, providing valuable methodological support for reliability optimization of hydraulic systems.

引用

页数：34

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