Hybrid multiobjective evolutionary algorithm based on differential evolution for flow shop scheduling problems

Abstract

This paper proposes a hybrid multiobjective evolutionary algorithm based on differential evolution (HMOEA/DE) to solve the flow shop scheduling problems (FSPs) with the criteria of minimizing the makespan and tardiness simultaneously. Firstly, the hybrid multiobjective evolutionary algorithm (HMOEA) in HMOEA/DE is designed as the global search strategy, which rapidly improves the convergence and distribution performances towards the center and edge regions of Pareto frontier. Secondly, differential evolution (DE) strategy is combined with HMOEA as the local search mechanism to further enhance the convergence and distribution performances on the elite population obtained by HMOEA. Two DE mutation operators are designed for the individuals in the elite population: one is to further improve the performance of each individual and the other serves to much enhance the individual randomly. Numerical comparisons indicate that the efficacy of HMOEA/DE outperforms the traditional multiobjective evolutionary algorithm without DE in convergence and distribution performances on benchmark test problems and FSPs while verifying the advantages and disadvantages of different DE methods.