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A version of cooperative multi-swarm PSO using electoral mechanism to solve hybrid flow shop scheduling problem

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Warianty tytułu
PL
Kooperacyjny algorytm rojowy PSO wykorzystujący mechanizm wyboru do rozwiązywania problemów harmonogramu w systemie HFS
Języki publikacji
EN
Abstrakty
EN
Hybrid flow shop (HFS) scheduling problem is a kind of scheduling consisted of a series of stages, in which there exist more than one parallel machine. In this paper, we propose a meta-heuristics using a version of cooperative multi-swarm PSO algorithm for the HFS with minimum makespan objective. The main contribution of this algorithm is to import an electoral mechanism to accelerate the converging and a disturbance approach to help escape from local optima. Finally, experiments show that the algorithm outperforms all the compared in the HFS problem.
PL
W artykule zaproponowano nowy rojowy algorytm do rozwiązywania problemu w harmonogramie dostępu typu HFS. Nowy mechanizm wyboru pozwala na przyśpieszenie konwergencji.
Rocznik
Strony
22--26
Opis fizyczny
Bibliogr. 12 poz., rys., tab.
Twórcy
autor
autor
  • Anhui Science and Technology University
Bibliografia
  • [1] Gupta J.N.D., Two-stage hybrid flow shop scheduling problem, Journal of the Operational Research Society, 39 (1988), 359-364
  • [2] Janiak A., Kozan E., Lichtenstein M., Oğuz C., Metaheuristic approaches to hybrid flow shop scheduling problem with a costrelated criterion, International Journal of Production Economics, 105 (2007), 407-424
  • [3] Engin O., G. Ceran, M.K. Yilmaz, An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems, Applied Soft Computing, (2011), doi:10.1016/j.asoc.- 2010.12.006
  • [4] Liao C.J., Tseng C.T., P. Luarn, A discrete version of particle swarm optimization for flowshop scheduling problems, Computers and Operations Research, 34 (2007), 3099-3111
  • [5] Sha D.Y., Hsu C.Y., A new particle swarm optimization for the open shop scheduling problem, Computers and Operations Research, 35 (2008), 3243-3261
  • [6] Carlier J., Néron E., An exact method for solving the multiprocessor flowshop, R.A.I.R.O: Operations Research, 34 (2000), 1-25
  • [7] Néron E., Baptise P., Gupta J.N.D., Solving hybrid flow shop problem using energetic reasoning and global operations, Omega, 29 (2001), 501-511
  • [8] Kennedy J., Eberhart R.C., Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Network, (1995), 1942–1948
  • [9] Van den Bergh F., Engelbrecht A.P., Effects of swarm size o cooperative particle swarm optimizers. Proceedings of the GECCO. (2001), 892-899
  • [10] Yu B., Jiao B., Gu X., An Improved Cooperative Particle Swarm Optimization and Its Application to Flow Shop Scheduling Problem, Journal of East China University of Science and Technology (Natural Science Edition), 35 (2009), 468-474
  • [11] Kahraman C., Engin O., Kaya I., Yilmaz M.K.. An application of effective genetic algorithms for Solving Hybrid Flow Shop Scheduling Problems, International Journal of Computational Intelligence Systems, 35 (2008), 134-147
  • [12] Néron E., Baptise P., Gupta J.N.D., Solving hybrid flow shop problem using energetic reasoning and global operations, Omega, 29 (2001), 501-511
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOK-0037-0005
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