Mohadeseh Soleimanpour-Moghadam

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About

Mohadeseh Soleimanpour-Moghadam is a researcher specializing in multi-robot systems and optimization algorithms, with a particular focus on task allocation—a critical challenge in coordinating autonomous robotic teams. Her work addresses how multiple robots can efficiently divide and execute complex tasks in dynamic environments. In her highly cited 2020 paper, she introduced a Discrete Genetic Algorithm (DGA) that adapts classical genetic algorithms to the discrete nature of multi-robot task allocation, enabling more effective population generation and solution convergence. This work has garnered 12 citations, reflecting its significance in the field. Building on this, her 2021 paper proposed a novel task allocation algorithm inspired by universal gravity rules, offering a physics-based approach to robot coordination that has earned 8 citations. Soleimanpour-Moghadam’s contributions are notable for bridging bio-inspired computation with practical robotics, providing scalable and robust solutions for real-world applications like search-and-rescue, warehouse automation, and exploration. Her research continues to influence the development of intelligent, decentralized multi-robot systems, making her a rising voice in autonomous robotics and swarm intelligence.