Papers
4
Total Citations
137
H-Index
4
About
Qifang Luo is a computational intelligence researcher specializing in metaheuristic optimization algorithms and their real-world engineering applications. His work sits at the intersection of swarm intelligence, bio-inspired computing, and robotics, with a particular focus on developing and refining nature-mimicking algorithms to solve complex optimization problems. Luo's most significant contributions center on advancing the Slime Mould Algorithm (SMA), a cutting-edge metaheuristic framework. His highly cited DTSMA (2022, 60 citations) addressed critical limitations in SMA by introducing dominant swarm strategies and adaptive T-distribution mutation, substantially improving the balance between exploration and exploitation. Building on this, his hybrid Equilibrium Optimizer Slime Mould Algorithm (EOSMA, 36 citations) demonstrated practical impact by efficiently solving inverse kinematics problems for complex 7-DOF robotic manipulators. Beyond SMA, Luo has pioneered novel bio-inspired approaches, including the Polar Coordinate Bald Eagle Search algorithm for curve approximation (22 citations) and a hybrid Whale-Firefly algorithm for mobile robot path planning (19 citations). Collectively, his research reflects a consistent commitment to bridging theoretical algorithmic innovation with tangible engineering solutions, making him a notable contributor to the rapidly evolving field of computational optimization.
Research Focus
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