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About

Saad Harous is a leading researcher in multi-robot systems and artificial intelligence, with a primary focus on solving the computationally complex Multi-Robot Task Allocation (MRTA) problem. His work addresses the fundamental challenge of optimally assigning tasks to robots under real-world constraints, a problem known to be NP-hard. Harous has pioneered distributed solutions that combine bio-inspired algorithms—such as Ant Colony Optimization, the Bat Algorithm, and the Consensus-Based Bundle Algorithm—to achieve efficient, scalable task allocation without centralized control. His 2020 paper on a distributed approach using the Consensus-Based Bundle Algorithm and Ant Colony System has garnered 76 citations, reflecting its significant impact on the field. He further advanced the state of the art by introducing the FA–QABC–MRTA framework (40 citations), which integrates the Firefly Algorithm with a quantum-inspired artificial bee colony. Beyond MRTA, Harous has contributed a comprehensive survey on Deep Reinforcement Learning applications in autonomous systems (2025), highlighting future directions for autonomous vehicles, robotics, and drones. His work also includes formal analyses using set theory to rigorously define MRTA constraints, demonstrating a commitment to both practical solutions and theoretical foundations. Harous’s research is essential reading for anyone working on autonomous multi-robot coordination.