Guanglei Sheng
Papers
1
Total Citations
40
H-Index
1
About
Guanglei Sheng is a rising computational intelligence researcher whose work focuses on advancing metaheuristic optimization algorithms for complex engineering challenges. His primary research areas include swarm intelligence, evolutionary computation, and multi-objective optimization, with a particular emphasis on adaptive and co-evolutionary systems. Sheng’s most notable contribution is the development of ACEPSO (Adaptive Co-Evolved Particle Swarm Optimization), a novel algorithm that integrates multiple adaptive strategies and co-evolutionary mechanisms to enhance solution accuracy and convergence speed. Published in 2024, this work has already garnered 40 citations, reflecting its immediate impact on the optimization community. ACEPSO demonstrates superior performance in solving constrained engineering problems, such as structural design and parameter tuning, outperforming traditional PSO variants. Sheng’s research bridges the gap between theoretical algorithm design and practical application, offering robust tools for real-world engineering optimization. His work is particularly valuable for students and researchers exploring adaptive mechanisms in swarm intelligence, as it provides a clear framework for enhancing algorithm adaptability. With his innovative approach and growing citation record, Sheng is establishing himself as a promising contributor to the field of computational optimization.
Research Focus
Key Achievements
Top Papers
- 1