A Multi-Agent Collaborative Work Planning Strategy Based on AFSA-PSO Algorithm

Agent and multi-agent technology originates from the research of distributed artificial intelligence. The traditional control methods of multi-agent system are centralized and hierarchical from top to bottom, which violates the idea of the adaptability, flexibility and relative independence of agents, and has the fatal defect of over-dependence on the main controller. In this paper, an artificial fish swarm algorithm and particle swarm optimization algorithm are combined to find the optimal solution and solve the problem of multi-agent cooperative work and path planning. Simulation results show that the multi-agent cooperative evolution based on AFSA-PSO algorithm can adapt to the dynamic changes of new tasks, and has not only a good global convergence, but also a fast local convergence rate, simple operation and few parameters.