Jiaqi Fan
Papers
2
Total Citations
18
H-Index
2
About
Jiaqi Fan is a researcher in swarm intelligence and multi-robot systems, with a focus on developing and comparing learning algorithms for robot swarms. Their most cited work, "A comparison analysis of swarm intelligence algorithms for robot swarm learning" (2017), systematically evaluates how different swarm intelligence (SI) algorithms—such as particle swarm optimization (PSO), evolutionary algorithms, and Q-learning—perform in enabling robots to form swarms and share information autonomously. This study provides critical insights into the relative strengths of PSO, which was found to outperform other methods in certain swarm learning tasks. With a combined citation count of 18 for this paper, Fan’s contributions help guide the design of more efficient, decentralized learning strategies for robotic collectives. Their work is particularly valuable for researchers in robotics, artificial intelligence, and autonomous systems, offering a foundational comparison that informs algorithm selection in real-world swarm applications. Fan’s research continues to influence the development of scalable, intelligent robot teams capable of collaborative problem-solving.
Research Focus
Key Achievements
Top Papers
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