Toward a New Coordination Mechanism for Multi-Agent Systems Based on a Metaheuristic Approach
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Date
2026
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Abstract
Multi-agent systems (MAS) are now a critical tool for tackling complex problems in dynamic
environments. Because of their high dimensionality and the computational complexity of these
environments, it remains a significant challenge to coordinate these agents and plan their tasks and
paths. The ability of metaheuristic approaches to find optimal solutions has often made them a
promising option, but they struggle with issues like local optima, premature convergence, and a limited
balance between exploration and exploitation. This thesis tackles these challenges by developing and
evaluating the recent enhanced and hybridized metaheuristic algorithms tailored for solving MAS path
planning and task coordination. The main contributions of this thesis include a new method for Particle
Swarm Optimization (PSO), named IB-PSO, which presents a novel approach to dynamically increase
the inertia weight (IW) parameter inspired by the Butterworth function. It enhances the balance between
exploration and exploitation of the PSO algorithm. This method is designed to solve the MAS pathplanning
problem.
Furthermore,
the
research
provides
a
hybrid
approach
named
CSO,
which
combines
the
Crow
Search Algorithm (CSA) and PSO. It is particularly designed for MAS path planning with
collision avoidance, leveraging the strengths of CSA in exploration and PSO in exploitation, using the
velocity regulating method. The performance of these proposed methods is evaluated and compared
with existing enhanced techniques. Ultimately, this research introduces new optimization methods that
help advance the field of MAS coordination.