Hongwei Ding
Papers
9
Total Citations
156
H-Index
7
About
Hongwei Ding is a computational intelligence researcher whose work centers on metaheuristic optimization algorithms and autonomous robotics, with a particular focus on swarm intelligence and bio-inspired computing. His most significant contributions involve the development and refinement of the Salp Swarm Algorithm (SSA), where he has introduced innovative enhancements such as rank-driven strategies, orthogonal opposition-based learning, and velocity clamping mechanisms to address fundamental limitations in exploration-exploitation balance. His 2021 paper on the rank-driven SSA has garnered 47 citations, establishing him as a notable voice in the optimization community. Beyond swarm intelligence, Ding has made meaningful strides in physics-based optimization, producing multiple enhanced variants of the Equilibrium Optimizer that incorporate chaos theory, spiral search mechanisms, and hybrid frameworks combining moth flame optimization. His work consistently bridges theoretical algorithm design with real-world applications, most notably mobile robot path planning and deep reinforcement learning-based navigation systems. With cumulative citations approaching 160 across his recent publications, Ding's research demonstrates growing influence in addressing the persistent challenges of local optima avoidance and algorithmic convergence speed — problems central to deploying intelligent optimization in practical engineering contexts.
Research Focus
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Top Papers
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