Research on Multi-AGVs Scheduling Based on Genetic Particle Swarm Optimization Algorithm

Abstract

In order to solve the scheduling optimization problem of multiple automated guided vehicles (multi-AGVs) in the production workshop, an optimization model for the shortest AGV material transportation time is established and a genetic particle swarm optimization (GPSO) algorithm is proposed. In order to balance the global and local search capabilities of the algorithm, a multi-neighborhood topology scheme is designed. The best-worst crossover operation is used to ensure that the solutions produced are all feasible solutions. The improved mutation operation is designed to speed up the convergence speed and jump out of the local optimum. The proposed algorithm is compared with the particle swarm optimization algorithm, the ant colony optimization algorithm and the genetic algorithm. Experiments and comparisons have proved the feasibility and effectiveness of the improved algorithm.