Tanghuai Fan
Papers
2
Total Citations
15
H-Index
2
About
Tanghuai Fan is a researcher specializing in swarm intelligence algorithms and their application to robotic path planning. His primary contributions lie in advancing multi-objective optimization techniques, particularly through the development of the multi-objective firefly algorithm based on archive learning. This innovative approach addresses critical limitations in traditional firefly algorithms—namely, slow convergence and low solution precision—by preserving elite particles from each generation in an external archive and randomly selecting from them to guide the search process. Fan’s work has been recognized in the academic community, with his most-cited paper, "Application of multi-objective firefly algorithm based on archive learning in robot path planning" (2019), accumulating 9 citations, and a closely related publication garnering 6 citations. These studies demonstrate the practical impact of his research on improving the efficiency and accuracy of autonomous navigation systems. By enhancing the performance of metaheuristic algorithms, Fan’s contributions offer valuable tools for engineers and researchers tackling complex, real-world optimization problems in robotics and beyond.
Research Focus
Key Achievements
Top Papers
- 1
- 2