Xuezhen Cheng

Shandong University of Science and Technology

Papers

Total Citations

H-Index

About

Xuezhen Cheng is a leading researcher in intelligent robotics and swarm optimization, whose work has significantly advanced autonomous navigation systems. Her primary research focuses on developing hybrid metaheuristic algorithms for robot path planning, particularly addressing the critical challenges of premature convergence and limited global search capability in traditional optimization methods. Cheng's most influential contribution is the novel PSO-GWO algorithm, which synergistically combines particle swarm optimization with grey wolf optimizer while incorporating chaos theory and adaptive inertial weighting. This breakthrough approach, detailed in her 2021 paper that has garnered 69 citations, effectively prevents particles from becoming trapped in local optima, enabling more robust and efficient path planning in complex environments. Her work represents a substantial improvement over conventional PSO algorithms, offering enhanced convergence speed and solution quality for real-world robotic applications. Cheng's research has become essential reading for engineers and researchers working on autonomous systems, with her algorithm serving as a foundation for numerous subsequent studies in intelligent path planning. Her contributions continue to influence the development of more adaptive and reliable navigation solutions for mobile robots.