Zongshan Wang
Papers
11
Total Citations
182
H-Index
7
About
Zongshan Wang is a computational intelligence researcher specializing in swarm intelligence, metaheuristic optimization algorithms, and robotics path planning. His work has made significant contributions to the advancement of bio-inspired and physics-based optimization techniques, particularly through systematic enhancements of established algorithms to overcome their inherent limitations. Wang is best known for his extensive development of improved variants of the Salp Swarm Algorithm (SSA), introducing innovations such as rank-driven strategies, orthogonal opposition-based learning, and velocity clamping mechanisms to better balance exploration and exploitation — a central challenge in optimization research. His 2021 paper on the rank-driven SSA alone has garnered 47 citations, with related SSA studies collectively accumulating over 130 citations, underscoring the field's recognition of his contributions. More recently, Wang has turned his attention to the Equilibrium Optimizer (EO), proposing multiple enhanced variants incorporating chaos theory, spiral search mechanisms, and hybrid frameworks with moth flame optimization to address local optima stagnation and poor population diversity. His research extends into practical applications, including mobile robot path planning and multi-robot coordination in dynamic environments. Through consistent algorithmic innovation grounded in rigorous theoretical analysis and real-world case studies, Wang has established himself as a productive and impactful voice in evolutionary computation and intelligent optimization.
Research Focus
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