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About

Pranav Mehta is a rising force in the field of mechanical engineering optimization, specializing in the development of novel metaheuristic algorithms for solving complex, real-world design problems. His research focuses on enhancing the structural performance of engineering components by hybridizing physics-based and mathematics-inspired optimizers with oppositional-based learning techniques. Mehta’s most impactful contribution is the hybrid flow direction optimizer with dynamic oppositional-based learning (HFDO-DOBL), which has garnered 74 citations for its effectiveness in tackling constrained mechanical design challenges such as planetary gear trains and hydrostatic systems. He has also pioneered the Fick’s law algorithm with quasi-oppositional learning (38 citations) and the geometric mean optimizer for optimizing robot grippers, airplane brackets, and suspension arms (27 citations). His recent work on enhanced hippopotamus optimization integrated with artificial neural networks (2025, 12 citations) demonstrates his commitment to advancing AI-driven design. With over 166 citations across his top papers, Mehta’s algorithms are widely adopted by researchers seeking robust solutions to non-linear, non-convex engineering problems. His early survey on Arduino robotic hands (2018) reflects his foundational interest in mechatronics, but it is his innovative hybrid optimizers that establish him as a key contributor to modern mechanical design optimization.