Yuxue Song
Papers
1
Total Citations
57
H-Index
1
About
Yuxue Song is a researcher specializing in computational intelligence and swarm-based optimization algorithms, with a focus on autonomous target searching in unknown environments. Their most notable contribution is the development of a novel hybrid algorithm that integrates Particle Swarm Optimization (PSO) and Fruit Fly Optimization Algorithm (FOA), published in 2019. This work, which has garnered 57 citations, addresses critical challenges in dynamic, uncertain environments by enhancing convergence speed and search accuracy—key for applications in robotics and autonomous systems. Song’s research bridges theoretical algorithm design and practical deployment, offering robust solutions for real-time navigation and exploration tasks. By synergizing the strengths of PSO’s global search and FOA’s local exploitation, their hybrid approach has influenced subsequent studies in adaptive optimization. With a growing citation impact, Song’s work is recognized for its methodological innovation and applicability, marking them as a rising contributor to the fields of metaheuristic optimization and intelligent systems. Their research continues to inspire advancements in autonomous decision-making and environmental sensing.
Research Focus
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Top Papers
- 1