Xucheng Ning

Papers

Total Citations

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About

Xucheng Ning is a researcher specializing in intelligent optimization algorithms and their application to autonomous robotic systems. His primary research areas include swarm intelligence, path planning for mobile robots, and the enhancement of metaheuristic optimization techniques. Ning’s most notable contribution is his work on the modified grey wolf optimizer (GWO), where he addressed key limitations of the standard algorithm—such as premature convergence and poor local optima avoidance—to improve path planning efficiency for patrol robots. His 2023 paper on this topic has garnered 12 citations, reflecting its relevance in the growing field of autonomous navigation. By refining the GWO’s structure while retaining its simplicity and few adjustable parameters, Ning has provided a practical tool for real-world robotics applications. His work stands out for its focus on overcoming specific algorithmic defects, offering a clear advancement over existing methods. This contribution is particularly valuable for students and researchers exploring optimization-driven robotics, as it demonstrates how targeted modifications to classic algorithms can yield significant performance gains in complex, dynamic environments.