Multiobjective Particle Swarm Optimization with Directional Search for Distributed Permutation Flow Shop Scheduling Problem

The distributed permutation flow shop scheduling problem (DPFSP) is a variant of the permutation flow shop scheduling problem (PFSP). DPFSP is closer to the actual situation of industrial production and has important research significance. In this paper, a multiobjective particle swarm optimization with directional search (MoPSO-DS) is proposed to solve DPFSP. Directional search strategy are inspired by decomposition. Firstly, MoPSO-DS divides the particle swarm into three subgroups, and three subgroups are biased in different regions of the Pareto front. Then, particles are updated in the direction of the partiality. Finally, combine the particles of the three subgroups to find the best solution. MoPSO-DS updates particles in different directions which speed up the convergence of the particles while ensuring good distribution performance. In this paper, MoPSO-DS is compared with the NAGA-II, SPEA2, MoPSO, MOEA/D, and MOHEA algorithms. Experimental results show that the performance of MoPSO-DS is better.

[1]  Xinjie Yu,et al.  Introduction to evolutionary algorithms , 2010, The 40th International Conference on Computers & Indutrial Engineering.

[2]  Ling Wang,et al.  A collaborative optimization algorithm for energy-efficient multi-objective distributed no-idle flow-shop scheduling , 2019, Swarm Evol. Comput..

[3]  Bin Li,et al.  Particle swarm optimization based clustering algorithm with mobile sink for WSNs , 2017, Future Gener. Comput. Syst..

[4]  Rubén Ruiz,et al.  The distributed permutation flowshop scheduling problem , 2010, Comput. Oper. Res..

[5]  Junfei Qiao,et al.  An Adaptive Multiobjective Particle Swarm Optimization Based on Multiple Adaptive Methods , 2017, IEEE Transactions on Cybernetics.

[6]  Mitsuo Gen,et al.  Hybrid sampling strategy-based multiobjective evolutionary algorithm for process planning and scheduling problem , 2014, J. Intell. Manuf..

[7]  Yuyan Han,et al.  Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions , 2018 .

[8]  Mitsuo Gen,et al.  Hybrid multiobjective evolutionary algorithm based on differential evolution for flow shop scheduling problems , 2019, Comput. Ind. Eng..

[9]  John A. W. McCall,et al.  D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces , 2014, Evolutionary Computation.

[10]  Min Dai,et al.  Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization , 2016, Comput. Ind..

[11]  Bassem Jarboui,et al.  Combinatorial particle swarm optimization for solving blocking flowshop scheduling problem , 2016, J. Comput. Des. Eng..

[12]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[13]  Fuqing Zhao,et al.  A factorial based particle swarm optimization with a population adaptation mechanism for the no-wait flow shop scheduling problem with the makespan objective , 2019, Expert Syst. Appl..

[14]  Ling Wang,et al.  Modified multiobjective evolutionary algorithm based on decomposition for low-carbon scheduling of distributed permutation flow-shop , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[15]  Roberto Hornero,et al.  A Novel Hybrid Swarm Algorithm for P300-Based BCI Channel Selection , 2019 .

[16]  Mitsuo Gen,et al.  Fast Multi-objective Hybrid Evolutionary Algorithm for Flow Shop Scheduling Problem , 2017 .

[17]  Peiyong Duan,et al.  An improved artificial bee colony algorithm for addressing distributed flow shop with distance coefficient in a prefabricated system , 2019, Int. J. Prod. Res..

[18]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[19]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

[20]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.