Yuxuan Shao
Papers
1
Total Citations
23
H-Index
1
About
Yuxuan Shao is a researcher in swarm intelligence and mobile robotics, with a focus on developing advanced metaheuristic optimization algorithms for real-world navigation challenges. Their most notable contribution is the enhancement of the Dung Beetle Optimization (DBO) algorithm, a bio-inspired metaheuristic widely used for solving complex optimization problems. In their highly cited 2023 paper, Shao identified a critical limitation in the standard DBO algorithm—its poor balance between global exploration and local exploitation, which frequently causes it to become trapped in local optima. To address this, they introduced a multi-strategy improved version of DBO, specifically tailored for obstacle avoidance in mobile robot navigation. This work has already garnered 23 citations, demonstrating its immediate impact on the field. By integrating multiple optimization strategies, Shao’s approach significantly enhances the algorithm’s ability to find optimal, collision-free paths in dynamic environments, offering a more robust solution for autonomous systems. Their research bridges the gap between theoretical optimization and practical robotics, providing a valuable tool for engineers and researchers working on intelligent navigation systems.
Research Focus
Key Achievements
Top Papers
- 1