Multidirection Update-Based Multiobjective Particle Swarm Optimization for Mixed No-Idle Flow-Shop Scheduling Problem

查看全文 作　　者：Wenqiang [1]Zhang;Wenlin [1]Hou;Chen [1]Li;Weidong [1]Yang;Mitsuo [2]Gen

机构地区：[1]the College of Information Science and Engineering,Henan University of Technology,and also with Henan Key Laboratory of Grain Photoelectric Detection and Control,Zhengzhou 450001,China;[2]the Fuzzy Logic Systems Institute,Fukuoka 820-0067,Japan,and also with Tokyo University of Science,Tokyo 162-8601,Japan

出　　处：《Complex System Modeling and Simulation》索引2021年第1卷第3期,共22页

基　　金：This work was partly supported by the National Natural Science Foundation of China(No.61772173);the Science and Technology Research Project of Henan Province(No.202102210131);the Innovative Funds Plan of Henan University of Technology(No.2020ZKCJ02);the Grant-in-Aid for Scientific Research(C)of Japan Society of Promotion of Science(No.19K12148). 摘　　要：The Mixed No-Idle Flow-shop Scheduling Problem(MNIFSP)is an extension of flow-shop scheduling,which has practical significance and application prospects in production scheduling.To improve the efficacy of solving the complicated multiobjective MNIFSP,a MultiDirection Update(MDU)based Multiobjective Particle Swarm Optimization(MDU-MoPSO)is proposed in this study.For the biobjective optimization problem of the MNIFSP with minimization of makespan and total processing time,the MDU strategy divides particles into three subgroups according to a hybrid selection mechanism.Each subgroup prefers one convergence direction.Two subgroups are individually close to the two edge areas of the Pareto Front(PF)and serve two objectives,whereas the other one approaches the central area of the PF,preferring the two objectives at the same time.The MDU-MoPSO adopts a job sequence representation method and an exchange sequence-based particle update operation,which can better reflect the characteristics of sequence differences among particles.The MDU-MoPSO updates the particle in multiple directions and interacts in each direction,which speeds up the convergence while maintaining a good distribution performance.The experimental results and comparison of six classical evolutionary algorithms for various benchmark problems demonstrate the effectiveness of the proposed algorithm. 关键词：multiobjective optimization Particle Swarm Optimization(PSO) Mixed No-Idle Flow-shop Scheduling Problem(MNLFSP) multidirection update

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Multidirection Update-Based Multiobjective Particle Swarm Optimization for Mixed No-Idle Flow-Shop Scheduling Problem

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