Papers

44

Total Citations

1,332

H-Index

15

About

Zhun Fan is a prolific researcher whose work spans evolutionary computation, mobile robotics, and intelligent systems engineering. He is perhaps best known for his contributions to constrained multi-objective optimization, particularly his highly influential epsilon constraint-handling method integrated with the MOEA/D framework for solving problems with large infeasible regions — a paper that has accumulated over 415 citations and stands as a landmark contribution to the evolutionary algorithm community. His robotics research is equally impressive, encompassing path planning algorithms, Mecanum-wheeled mobile robot control, multi-robot formation systems, and practical hospital service robotics, with his PQ-RRT* path planning work attracting over 264 citations. Fan has also made meaningful strides in applied computer vision, developing lightweight deep learning architectures for automated pavement crack detection and advancing swarm robotics through genetic programming. His early work on hospital transportation automation and multi-floor building mapping demonstrated a strong commitment to real-world deployment of robotic systems. More recently, Fan has explored modular design automation for intelligent robots, reflecting a broadening research vision. With thousands of citations across diverse domains, his work bridges theoretical optimization and practical robotics, making him a valuable figure for students and researchers in autonomous systems and computational intelligence.

Research Focus

Key Achievements

15
H-Index
44
Papers
1,332
Total Citations
30
Avg Citations/Paper
🏆 Most Cited Paper
An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions
415 citations · 2019
📈 Most Prolific Year: 2023 (9 Papers)
🤝 Key Collaborators: 157
🏛 Institutions: Shantou University, Technical University of Denmark, Tongji University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago