BENACHI Romaissa OULD AMAR Ines2025-02-092025-02-092024http://dspace.univ-khenchela.dz:4000/handle/123456789/7806Abstract : The limitations of traditional optimization techniques in addressing complex problems within multi-agent systems (MAS) necessitate the exploration of innovative approaches.This work delves into the potential of metaheuristics, a family of algorithms inspired by natural processes. We specifically investigate the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) as promising tools for MAS path planning. By incorporating the exploration-exploitation balance inherent to these metaheuristics, we aim to develop robust and adaptable path planning strategies that empower agents within MAS to navigate complex environments effectively.enThesis