Optimization of JSP Based on Particle Swarm Algorithm with Oscillation Regulation Mutation

Abstract

To prevent the premature phenomenon during the process of solving the job shop scheduling problem (JSP) by particle swarm optimization algorithm, an improved particle swarm optimization algorithm with oscillation regulation mutation is proposed by combining the oscillation regulation speed/position update method with the particle search neighborhood mutation. In order to verify the effect of the improved algorithm on JSP solution optimization comparing to the standard one, systematic studies are carried out by analyzing the typical cases of FT06, FT10, and FT20. The results show that the particle swarm optimization algorithm with oscillation regulation mutation can achieve more than 50% improvement in optimization efficiency, and can effectively constrain the risk of falling into the local optimal trap. In addition, compared with the standard particle swarm algorithm, the improved algorithm also has significant advantages in maintaining the optimization stability for solving the job shop scheduling problem.