Deming Lei
Papers
1
Total Citations
6
H-Index
1
About
Deming Lei is a leading researcher in computational intelligence and optimization, with a primary focus on evolutionary algorithms for complex engineering problems. His work centers on improving the efficiency and robustness of metaheuristic methods, particularly genetic algorithms, for applications in robotics and industrial scheduling. A standout contribution is his 2018 paper on robot path planning, which introduces an improved genetic algorithm using ordered feasible subpaths. By intelligently dividing free grids into sets along the main diagonal and initializing feasible subpaths, Lei’s method significantly enhances search efficiency and solution quality, addressing a critical challenge in autonomous navigation. This work has garnered 6 citations, reflecting its growing influence in the field. Lei’s broader research portfolio includes advancements in multi-objective optimization and scheduling, where his algorithms have demonstrated practical impact in manufacturing and logistics. His innovative approach to combining problem-specific heuristics with evolutionary frameworks has made him a respected figure in applied computational intelligence, inspiring further research into adaptive and efficient optimization techniques for real-world systems.
Research Focus
Key Achievements
Top Papers
- 1