Chaoli Tang
Papers
1
Total Citations
7
H-Index
1
About
Chaoli Tang is an emerging researcher in computational intelligence and robotics, with a primary focus on optimization algorithms and autonomous navigation. Their most impactful work centers on enhancing metaheuristic optimization methods for real-world engineering applications, particularly in mobile robot path planning. Tang’s highly cited 2024 study introduces a multi-strategy improved Harris Hawk Optimization (HHO) algorithm, addressing critical limitations of the original HHO—namely low solution accuracy, slow convergence, and a tendency to become trapped in local optima. By integrating novel enhancement mechanisms, this work achieves more efficient and reliable path planning, a cornerstone challenge in autonomous robotics. With 7 citations already, this paper demonstrates growing influence in the optimization and robotics communities. Tang’s contributions are particularly valuable for students and researchers seeking robust, nature-inspired algorithms for complex navigation tasks. Their work bridges theoretical algorithm design with practical deployment, offering tangible improvements for autonomous systems. As a rising voice in intelligent optimization, Chaoli Tang continues to advance the frontier of adaptive, high-performance path planning solutions.
Research Focus
Key Achievements
Top Papers
- 1