Hybrid flow-shop scheduling method based on multi-agent particle swarm optimization

In this paper, a multi-agent particle swarm optimization (MPSO) based on multi-agent system (MAS) and PSO was proposed for hybrid flow-shop scheduling problem (HFSP), and a random cycle topological structure is presented related to MPSO. In MAS, every particle represents an agent, and it can cooperate and compete with the agent around and do self-learning. Using these agent interactions and the evolution mechanism of PSO, MPSO can find the global optimum more accurately. The result of simulation proved that this algorithm has a higher searching efficiency and better optimal searching performance.