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About

Dr. Zulfiqar Ibrahim Bibi Farouk is a robotics researcher whose work centers on solving complex kinematic and dynamic problems in robot manipulators through advanced swarm intelligence. His primary contribution is the development of the Mutating Particle Swarm Optimization (MuPSO) algorithm, a robust enhancement of standard PSO designed to overcome convergence failures in multi-degree-of-freedom systems. His most cited paper, "A Novel Mutating PSO Based Solution For Inverse Kinematic Analysis Of Multi Degree-Of-Freedom Robot Manipulators" (2019, 11 citations), demonstrates how parameter modification enables reliable solutions for complex robot structures. Dr. Farouk further refined this approach in "Developing a New Robust Swarm-Based Algorithm for Robot Analysis" (2020, 10 citations), addressing the limitations of problem-specific metaheuristics by creating a more adaptable framework. His work on "Industrial manipulator dynamic parameter estimation using mutating particle swarm optimization" (2021) extends MuPSO to dynamic modeling, using finite Fourier series for excitation trajectory design. More recently, he has explored MRI-conditional neurosurgery robots (2022, 5 citations), bridging his optimization expertise with medical robotics. With a focused body of work that systematically advances swarm-based robot analysis, Dr. Farouk is establishing himself as a specialist in algorithm adaptation for real-world robotic systems.