A Swarm Intelligence Based Coordination Algorithm for Distributed Multi-Agent Systems
Yan Meng, Ọlọrundamilọla Kazeem, Juan C. Muller · 2007
This paper presents a synergy of ant colony optimization (ACO) and particle swarm optimization (PSO) into a novel hybrid coordination algorithm for distributed multi-agent systems. The intended multi-agent systems are composed of relatively simple, expendable agents with highly decentralized, self-organized behaviors; which as a whole achieve global optimization over a set task. Basically, two coordination processes among the agents will be established. One is a stigmergy-based algorithm using the distributed virtual pheromones to guide the agents' movement, the other one is interaction-based algorithm, where a global maximum of the attribute values can be obtained through the interaction between the agents. The simulation results demonstrate that the proposed hybrid swarm intelligence based architecture is feasible, efficient, and robust to coordinate a simulated distributed multi-agent system