A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem

作者：Wang, Chun; Ji, Zhicheng; Wang, Yan^*

来源：Mathematical Problems in Engineering, 2017, 2017: 2857564.

DOI：10.1155/2017/2857564

• GENETIC ALGORITHM
• EVOLUTIONARY ALGORITHM
• SEARCH ALGORITHM
• OPTIMIZATION ALGORITHM
• LOCAL SEARCH
• TABU SEARCH
• HYBRID
• MOEA/D
• HYBRIDIZATION

A novel multiobjective memetic algorithm based on decomposition (MOMAD) is proposed to solve multiobjective flexible job shop scheduling problem (MOFJSP), which simultaneously minimizes makespan, total workload, and critical workload. Firstly, a population is initialized by employing an integration of different machine assignment and operation sequencing strategies. Secondly, multiobjective memetic algorithm based on decomposition is presented by introducing a local search to MOEA/D. The Tchebycheff approach of OEA/D converts the three-objective optimization problem to several single-objective optimization subproblems, and the weight vectors are grouped by K-means clustering. Some good individuals corresponding to different weight vectors are selected by the tournament echanism of a local search. In the experiments, the influence of three different aggregation functions is first studied. Moreover, the effect of the proposed local search is investigated. Finally, MOMAD is compared with eight state-of-the-art algorithms on a series of well-known benchmark instances and the experimental results show that the proposed algorithm outperforms or at least has comparative performance to the other algorithms.

• 出版日期2017
• 单位江南大学