Coordination in Multi-Agent Systems: Overview of Metaheuristic Approaches

Background: In the field of distributed artificial intelligence, multi-agent systems (MAS) collaborate to resolve complex problems.To operate efficiently, these systems require effective coordination mechanisms.Metaheuristic algorithms are now standard for decentralized optimization and agent task distribution.Objective: This study evaluates the strengths and limitations of common metaheuristics used for MAS path planning and task coordination.Additionally, it introduces comparative simulation studies to evaluate the performance of baseline and dynamic metaheuristic methods specifically in MAS path planning.Methods: This paper presents a comprehensive narrative review of metaheuristic approaches for MAS coordination to identify current research gaps.It evaluates recent literature to identify their strengths, weaknesses and future trends.For practical validation, this review is coupled with a comparative simulation study using NetLogo to evaluate 50 agents in a 2D grid.The study specifically compares the standard crow search algorithm (SCSA) and standard particle swarm optimization (PSO) against their adaptive and modified versions (AAP-CSA, DEC-DAPCSA, MCSA, BPSO and LDPSO).This dual approach effectively bridges the gap between theoretical overview and practical performance evaluation. Results:The findings indicate that metaheuristic algorithms significantly affect MAS performance.Specifically, standard algorithms (SCSA and PSO) often trap in local optima, but their dynamic variants improve both fitness and convergence speed.Conclusion: The evaluated dynamic parameter strategies outperformed standard versions in both solution quality and search time.Ultimately, selecting the right metaheuristic and applying dynamic parameter control are essential for solving complex MAS coordination problems.