Papers
1
Total Citations
2
H-Index
1
About
Deqi Ming is a researcher focused on intelligent optimization algorithms and their applications in robotics, particularly in path planning for complex environments. His most-cited work, "Application of multi adaptive particle swarm optimization in robot path planning" (2022, 2 citations), addresses a critical limitation of standard particle swarm optimization—its tendency to converge prematurely and get trapped in local optima. Ming introduced a novel multi adaptive particle swarm optimization algorithm, featuring the innovative concept of "particle evolution degree," which dynamically adjusts search behavior to enhance exploration and exploitation. This contribution improves the efficiency and reliability of autonomous navigation systems, offering a more robust solution for real-world robotic tasks. While his citation count is still growing, Ming's work represents a meaningful step forward in adaptive optimization, with potential applications in autonomous vehicles, industrial robotics, and AI-driven control systems. His research bridges theoretical algorithm design and practical engineering challenges, making it relevant for students and researchers interested in swarm intelligence, evolutionary computation, and mobile robotics.
Research Focus
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Top Papers
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