Papers
6
Total Citations
148
H-Index
5
About
Yongquan Zhou is a computational intelligence researcher whose work centers on swarm intelligence, metaheuristic optimization algorithms, and autonomous robotics systems. Over more than fifteen years of active research, Zhou has made significant contributions to the design, hybridization, and application of nature-inspired algorithms, drawing inspiration from biological phenomena ranging from slime mould oscillations to bald eagle predation spirals. Among his most influential contributions is the development of the Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm (DTSMA), which addresses critical limitations in exploration-exploitation balance and local optima avoidance, earning 60 citations since its 2022 publication. His hybrid Equilibrium Optimizer Slime Mould Algorithm (EOSMA) demonstrated practical impact by efficiently solving inverse kinematics for complex 7-DOF robotic manipulators, accumulating 36 citations. Zhou has also advanced bio-inspired curve approximation and robot path planning, proposing novel frameworks such as the polar coordinate Bald Eagle Search and whale-firefly hybrid algorithms. His trajectory from early work on artificial fish-swarm navigation in 2008 to recent multi-robot path planning systems reflects a sustained commitment to bridging theoretical algorithm design with real-world engineering challenges, establishing him as a productive voice in the evolutionary computation and intelligent robotics communities.
Research Focus
Key Achievements
Top Papers
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- 6Path planning of robot based on artificial fish-swarm algorithm2 citations · 2008