Dalila Boughaci

University of Sciences and Technology Houari Boumediene

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About

Dalila Boughaci is a leading researcher in artificial intelligence and optimization, whose work bridges the gap between algorithmic theory and real-world multi-agent systems. Her primary research areas include metaheuristics, combinatorial optimization, and multi-robot task allocation, where she addresses the complex challenge of coordinating autonomous agents under stringent constraints. Boughaci’s most influential contributions focus on time-extended multi-robot task allocation problems, where tasks have both spatial and temporal requirements and agents possess limited capacities. Her 2018 paper, *Iterated Local Search for Time-extended Multi-robot Task Allocation with Spatio-temporal and Capacity Constraints* (24 citations), introduces a powerful local search method that maximizes task completion in over-constrained environments. Earlier, her 2015 work, *Efficient heuristics for a time-extended multi-robot task allocation problem* (12 citations), laid the groundwork by defining the problem and designing computationally efficient heuristics for resource-limited settings. These contributions are vital for applications in logistics, disaster response, and autonomous systems, demonstrating how clever algorithmic design can solve practical coordination problems. Boughaci’s research continues to inspire advances in intelligent agent coordination, making her a key figure in the field.