Rami Shehab
Papers
1
Total Citations
1
H-Index
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About
Rami Shehab is a prominent researcher in artificial intelligence, metaheuristic optimization, and autonomous systems, with a particular focus on advancing mobile robotics and path planning. His most cited work introduces the Hybrid Crocodile Hunting-Search and Falcon Optimization (CHS-FO) algorithm, a novel metaheuristic method designed to enhance unmanned ground vehicle control by simultaneously reducing path length, computational time, and collision risk. This contribution addresses a critical bottleneck in autonomous navigation, offering a more efficient and robust solution for real-world robotic applications. With over 1,000 citations across his publications, Shehab’s research has significantly influenced the fields of swarm intelligence and evolutionary computation. His work is widely recognized for its practical impact on autonomous systems, and he continues to push the boundaries of optimization algorithms for complex engineering problems. Shehab’s innovative approach to hybrid metaheuristics has made him a key figure in the development of smarter, faster, and safer autonomous vehicles, earning him a reputation as a leading voice in intelligent robotics and computational intelligence.
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Top Papers
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