Mahdi Yaghoobi

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About

Mahdi Yaghoobi is a researcher whose work sits at the intersection of robotics, control systems, and nature-inspired optimization algorithms. His primary research areas include mobile robot navigation, intelligent control for manipulator robots, and the development of novel metaheuristic algorithms for complex engineering problems. Yaghoobi’s most cited work, "Mobile robot navigation based on Fuzzy Cognitive Map optimized with Grey Wolf Optimization Algorithm used in Augmented Reality" (2017, 15 citations), demonstrates his innovative approach to integrating augmented reality with fuzzy logic and swarm intelligence for autonomous navigation. He has also contributed to the advancement of adaptive neuro-fuzzy inference systems (ANFIS) for manipulator robot control, introducing a particle swarm optimization-based approach to handle nonlinear, multi-variable systems. Most recently, Yaghoobi has pushed the boundaries of multimodal optimization with his "Multimodal Lotus Effect Algorithm for Engineering Optimization Problems" (2025), a novel bio-inspired method designed to simultaneously identify multiple optimal solutions in fields like game theory and robotics. This latest work highlights his ongoing commitment to solving challenging, real-world optimization problems through creative algorithmic design.