Yousra Alkabani

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About

Yousra Alkabani is a researcher whose work lies at the intersection of swarm robotics and evolutionary computation, focusing on how simple, decentralized robots can solve complex collective tasks. Her key contributions center on developing adaptive algorithms for two fundamental swarm behaviors: obstacle avoidance and foraging. In her most-cited work, "A distributed genetic algorithm for swarm robots obstacle avoidance" (2014, 9 citations), she pioneered a method that uses an evolutionary algorithm to train robots to navigate cluttered environments, addressing the computational bottleneck of the evaluation module. This approach allows robots to learn obstacle avoidance behaviors without centralized control. Her second notable paper, "Tornado: A Robust Adaptive Foraging Algorithm for Swarm Robots" (2013, 4 citations), tackles the benchmark foraging problem—inspired by insect swarms cooperating to locate and transport food items. The Tornado algorithm enables robots with minimal communication and individual capability to efficiently search environments and retrieve resources. Alkabani’s work demonstrates how genetic algorithms can be practically applied to real-world robotic swarms, offering scalable solutions for tasks where individual robot intelligence is limited but collective behavior is powerful. Her research continues to influence the design of robust, adaptive multi-robot systems.