Guoqing Jin
Papers
1
Total Citations
4
H-Index
1
About
Dr. Guoqing Jin is a researcher whose work bridges computational intelligence and control systems engineering, with a particular focus on optimization algorithms for industrial applications. His most cited paper, "Genetic Simulated Annealing Algorithm Used for PID Parameters Optimization" (2009), introduces a novel hybrid approach that synergizes genetic algorithms with simulated annealing to fine-tune proportional-integral-derivative (PID) controllers—a cornerstone of automation and process control. This contribution addresses a critical challenge in control theory: achieving optimal parameter tuning without exhaustive manual intervention. By combining the global search capabilities of genetic algorithms with the local refinement strengths of simulated annealing, Jin’s method enhances system stability and performance, offering a practical solution for complex, nonlinear systems. Though his citation count (4) reflects a focused niche, the work’s impact lies in its methodological innovation, providing a template for hybrid optimization in engineering. Dr. Jin’s research underscores the value of interdisciplinary approaches in advancing control theory, making his contributions a valuable reference for students and researchers exploring intelligent optimization techniques in real-world applications.
Research Focus
Key Achievements
Top Papers
- 1