In order to solve the problem of automatic guided vehicle (AGV) path planning in green remanufacturing system, an adaptive algorithm for global path optimization of AGV based on particle swarm optimization (PSO) is proposed. This method not only integrates the advantages of genetic algorithm (GA) and particle swarm optimization (PSO), but also improves the slow search speed of traditional fusion algorithm in the early iteration stage. In order to improve the convergence accuracy of the algorithm in the later iteration stage, a dual crossover mutation strategy is proposed. The improved PSO-GA fusion algorithm has stronger search ability, faster evolution speed and higher convergence precision than the traditional PSO-GA fusion algorithm. In order to verify the superiority of the improved algorithm, the grid method is used to simulate the running environment of the auto-guided transport vehicle, and the four algorithms of standard particle swarm optimization, genetic algorithm, traditional PSO-GA fusion and improved PSO-GA fusion are solved by MATLAB. The experimental results show that the improved PSO-GA algorithm is feasible and effective.
