Mirong Feng
Papers
2
Total Citations
15
H-Index
2
About
Mirong Feng is a researcher specializing in swarm intelligence, multi-objective optimization, and their applications in robotics. Her most notable contribution is the development of a multi-objective firefly algorithm enhanced with archive learning, designed to overcome critical limitations in traditional firefly algorithms—namely, slow convergence and low solution precision. By storing elite particles from each generation in an external archive and using them to guide the search process, Feng’s approach significantly improves both the speed and accuracy of optimization. This work has been directly applied to robot path planning, demonstrating its practical value in generating efficient, collision-free routes in complex environments. Her two most-cited papers, both published in 2019, have accumulated a combined 15 citations, reflecting growing interest in her methods among researchers working on intelligent robotics and evolutionary computation. Feng’s contributions are particularly relevant for students and researchers seeking robust, learning-based strategies for multi-objective problems, offering a clear example of how archive mechanisms can enhance metaheuristic performance in real-world engineering tasks.
Research Focus
Key Achievements
Top Papers
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