Papers
2
Total Citations
6
H-Index
2
About
Shuo Ji is a rising researcher at the intersection of intelligent optimization, robotics, and sustainable manufacturing. Their work focuses on developing advanced algorithms and robotic systems to solve complex real-world engineering challenges. Ji’s most notable contribution is an improved multi-objective harmony search algorithm enhanced by reinforcement learning, designed to tackle automated assembly line balancing while simultaneously optimizing energy consumption—a critical step toward greener, more efficient production systems. This work has already garnered early citations, signaling its relevance to both academia and industry. In the field of rehabilitation robotics, Ji led the design and optimization of EEGO (Electric Easy Go), a multifunctional human motion rehabilitation training robot capable of transforming between four working modes: Supporting Posture, Grasping Posture, and Riding. This innovation demonstrates a practical, patient-centered approach to assistive technology. With a growing citation record and a focus on impactful, cross-disciplinary problems—from algorithmic optimization to physical human-robot interaction—Shuo Ji is establishing a promising career dedicated to advancing intelligent systems for health and industry.
Research Focus
Key Achievements
Top Papers
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