Huiling Chen
Papers
2
Total Citations
88
H-Index
2
About
Huiling Chen is a leading figure in computational intelligence and optimization, with a research focus on metaheuristic algorithms, dimension reduction, and intelligent control systems. Chen’s early landmark work introduced an ant colony optimization-based dimension reduction method for high-dimensional datasets (2013, 64 citations), which provided a powerful new approach for tackling the curse of dimensionality in data mining and pattern recognition. More recently, Chen has advanced the field of robotics through an enhanced deep deterministic policy gradient (DDPG) algorithm for intelligent control of robotic arms (2023, 24 citations). This work addresses the critical challenge of poor robustness and adaptability in traditional control methods by designing a hybrid reward function and improving the experience replay mechanism, enabling more stable and adaptive real-time control. Chen’s contributions bridge the gap between nature-inspired optimization and deep reinforcement learning, offering practical solutions for complex, high-dimensional problems. With a growing citation record and a reputation for innovative algorithm design, Chen continues to influence both theoretical research and applied engineering in intelligent systems.
Research Focus
Key Achievements
Top Papers
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