Sadok Bouamama
Manouba University, Higher Colleges of Technology, University of Jeddah
Papers
7
Total Citations
121
H-Index
4
About
Sadok Bouamama is a robotics and artificial intelligence researcher whose work centers on multi-robot systems, swarm intelligence, and optimization algorithms. His most significant contributions lie in developing novel Particle Swarm Optimization (PSO) variants to address two of the field's most persistent challenges: multi-robot path planning and multi-robot task allocation (MRTA). Bouamama is perhaps best known for introducing the Dynamic Distributed Particle Swarm Optimization (D2PSO) algorithm, a framework designed to generate collision-free, optimal trajectories for large-scale robot teams — a problem that becomes computationally intractable with conventional approaches. His 2017 paper on this topic has accumulated 48 citations, reflecting its strong uptake in the robotics community. Building on this foundation, he extended his PSO-based methods to tackle MRTA, proposing automatic clustering techniques that intelligently map tasks to robots at scale, earning 40 citations for his 2019 work in this area. His more recent 2023 contribution on Evolutionary Swarm Robotics signals a continued evolution toward integrating evolutionary computation with swarm coordination. Across his body of work, Bouamama has established himself as a dedicated contributor to scalable, intelligent solutions for autonomous multi-robot coordination, with cumulative citations exceeding 120.
Research Focus
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Top Papers
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