Dehua Li

Huazhong University of Science and Technology

Papers

Total Citations

H-Index

About

Dehua Li’s research lies at the intersection of intelligent systems, optimization algorithms, and knowledge-based decision-making. His work addresses critical challenges in robotics and information fusion, particularly through the application of particle swarm optimization (PSO) to trajectory planning for robot manipulators. In his 2008 study, Li tackled the highly constrained, nonlinear trajectory planning problem (TPP), developing a PSO-based algorithm that minimizes total path motion while ensuring obstacle avoidance—a contribution that has garnered 5 citations and remains relevant for autonomous robotic systems. Li also advanced multi-source information integration by addressing the computational bottlenecks of Dempster-Shafer theory. His 2002 paper introduced a plausibility measure to reduce the geometric time complexity associated with aggregating evidence from numerous sources, earning 5 citations for its practical impact on intelligent system design. Additionally, Li explored knowledge extraction in learning classifier systems (XCS), focusing on rule set compaction for non-Markov multi-step problems (2013). While his citation counts are modest, his work demonstrates a sustained commitment to solving foundational problems in optimization and evidence fusion, offering valuable insights for researchers in robotics and artificial intelligence.