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 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.