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Related Experiment Video

Updated: Jun 10, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Q-learning improved golden jackal optimization algorithm and its application to reliability optimization of hydraulic

Dongning Chen1, Haowen Wang2, Dongbo Hu2

  • 1School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, China. dnchen@ysu.edu.cn.

Scientific Reports
|October 19, 2024
PubMed
Summary

A new Q-learning Improved Gold Jackal Optimization (QIGJO) algorithm enhances intelligent movement and optimization performance. This advanced method improves convergence accuracy and global exploration for complex engineering problems, including hydraulic system reliability.

Keywords:
Global optimizationGolden jackal optimizationQ-LearningReliability optimization

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Area of Science:

  • Artificial Intelligence
  • Optimization Algorithms
  • Reliability Engineering

Background:

  • Golden Jackal Optimization (GJO) requires enhanced intelligent movement and performance.
  • Existing optimization techniques may lack sufficient global exploration capability.
  • Reliability of hydraulic systems in concrete pump trucks is critical.

Purpose of the Study:

  • To propose the Q-learning Improved Gold Jackal Optimization (QIGJO) algorithm.
  • To enhance the performance and intelligent movement behavior of the GJO algorithm.
  • To apply QIGJO for the reliability optimization of hydraulic systems in concrete pump trucks.

Main Methods:

  • Developed QIGJO by integrating five update mechanisms and a double-population Q-learning collaborative mechanism.
  • Introduced a novel convergence factor to improve GJO's convergence capability.
  • Established a reliability optimization model using Continuous-time Multi-dimensional T-S dynamic Fault Tree (CM-TSdFT), considering operating time and impact factors.

Main Results:

  • QIGJO demonstrated superior performance on 23 benchmark functions and CEC2022.
  • The algorithm exhibited high convergence accuracy and significantly enhanced global exploration.
  • Optimizing the hydraulic system reliability model with QIGJO yielded excellent results.

Conclusions:

  • QIGJO offers a significant improvement over standard GJO.
  • The proposed algorithm provides a robust approach for complex optimization tasks.
  • QIGJO offers valuable methodological support for the reliability optimization of hydraulic systems.