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

•Detecting location of individual in Pareto front by fitness function in HMOEA.•Converging to the multi-area of Pareto front by elitist and selection strategies.•DE enhances the local search on elitist derived from HMOEA.•Two DE mutation operators are designed for the individuals in elite population...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 130; pp. 661 - 670
Main Authors: Zhang, Wenqiang, Wang, Yu, Yang, Yang, Gen, Mitsuo
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2019
Subjects:
Differential evolution
Flow shop scheduling problem
Multiobjective evolutionary algorithm
Multiobjective evolutionary algorithm
Differential evolution
Flow shop scheduling problem
ISSN:0360-8352, 1879-0550
Online Access:Get full text
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Summary:•Detecting location of individual in Pareto front by fitness function in HMOEA.•Converging to the multi-area of Pareto front by elitist and selection strategies.•DE enhances the local search on elitist derived from HMOEA.•Two DE mutation operators are designed for the individuals in elite population.•Numerical comparisons indicate efficacy of HMOEA/DE on benchmark and FSP. 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.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2019.03.019