Multiobjective Particle Swarm Optimization with Directional Search for Distributed Permutation Flow Shop Scheduling Problem

Abstract

The distributed permutation flow shop scheduling problem (DPFSP) is a variant of the permutation flow shop scheduling problem (PFSP). DPFSP is closer to the actual situation of industrial production and has important research significance. In this paper, a multiobjective particle swarm optimization with directional search (MoPSO-DS) is proposed to solve DPFSP. Directional search strategy are inspired by decomposition. Firstly, MoPSO-DS divides the particle swarm into three subgroups, and three subgroups are biased in different regions of the Pareto front. Then, particles are updated in the direction of the partiality. Finally, combine the particles of the three subgroups to find the best solution. MoPSO-DS updates particles in different directions which speed up the convergence of the particles while ensuring good distribution performance. In this paper, MoPSO-DS is compared with the NAGA-II, SPEA2, MoPSO, MOEA/D, and MOHEA algorithms. Experimental results show that the performance of MoPSO-DS is better.