MOEA/D for flowshop scheduling problems

Abstract

Many multiobjective evolutionary algorithms are based Pareto domination, among them NSGA II and SPEA 2 are two very popular ones. MOEA/D is a very recent multiobjective evolutionary algorithm using decomposition. In this paper, we implement MOEA/D for multi-objective flowshop scheduling problems. We study the replacement strategy of neighboring solutions, the determination of the reference point, and compare different decomposition methods. Experimental results demonstrate that MOEA/D outperforms NSGA II and SPEA 2 significantly for the 2-objective and 3-objective benchmark flowshop-scheduling instances.