Serkan Dereli
Papers
11
Total Citations
437
H-Index
8
About
Serkan Dereli is a prominent researcher specializing in computational intelligence, swarm optimization algorithms, and robotic kinematics. His work is centered on solving one of robotics' most challenging problems — inverse kinematics for high-degree-of-freedom serial manipulators — through innovative applications of nature-inspired metaheuristic techniques. Dereli has made significant contributions by systematically benchmarking and adapting swarm intelligence methods, including Particle Swarm Optimization (PSO), the Firefly Algorithm, Artificial Bee Colony, and Grey Wolf Optimization, to address the complex, nonlinear inverse kinematics of 7-DOF redundant robotic arms. His most cited work, introducing a quantum-behaved PSO approach (143 citations), demonstrates both the breadth and depth of his expertise. Beyond applying existing algorithms, he has shown a creative flair for algorithmic innovation — developing novel variants inspired by golf ball dynamics and average swarm behavior to enhance optimization performance. With a cumulative citation count exceeding 430 across his top works, Dereli's research has garnered substantial recognition from the robotics and computational intelligence communities. His more recent investigations into FPGA-based hardware implementations signal a compelling evolution toward real-world, embedded robotic systems, making his work increasingly relevant for next-generation autonomous robotics applications.
Research Focus
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Top Papers
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