Research On Muti-AGV Sechduling Algorithm Based On Improved Hybird GA-PSO For FMS

The AGV system job scheduling in task assignment is the first problem to be solved in AGV application.AGV is often used to transport materials or products in the Flexible Manufacturing System(FMS),therefore,the optimized schedule of the AGV becomes the key to increase production efficiency.For AGV scheduling,AGV task allocation problem should be taken into account,and other factors such as the time spent on each operation and the running time of the car should be considered.Compared to single AGV scheduling algorithms,multi-AGV multi-task scheduling algorithms require a more complex model to support.In this paper,considering the electric quantity of AGV,the minimum completion time and the minimum number of AGV scheduling are the optimization targets.Meanwhile,the PSO and GA algorithms are improved respectively for the problems that the particle swarm optimization algorithm can easily fall into the local optimization and the genetic algorithm defect in searching accuracy and speed.In this paper,the two algorithms are used in a mixed way to realize the complementary advantages of the algorithm.The problem of local distribution of population particles due to the random distribution initiated by the particle swarm algorithm are solved by the balanced assessment method,and algorithmic efficiency problems due to cross-mutation operation with genetic algorithms with fixed probability are solved by adaptive genetic operator in this paper.An improved hybrid particle swarm optimization and genetic algorithm(PSO-GA)for FMS is proposed.This algorithm can give the optimal solution to the scheduling problem in a reasonable time.Based on the proposed scheduling algorithm,a multi-AGV scheduling model and a detailed scheduling algorithm flow are presented for FMS-oriented systems in this paper.Finally,this paper conducted a simulation experiment based on the improved hybrid PSO-GA for FMS.The experimental results show that compared with a single GA or PSO algorithm,the improved algorithm has obvious optimization effect in global optimization convergence and algorithm running time,and the search precision and convergence speed are further improved compared with the existing hybrid PSO-GA algorithm.