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A multipopulation PSO based memetic algorithm for permutation flow shop scheduling.

The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algor...

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Published in:Scientific World Journal Vol. 2013; pp. 387194 - 387195
Main Authors: Liu, Ruochen, Ma, Chenlin, Ma, Wenping, Li, Yangyang
Format: research Journal Article
Published: Wiley-Blackwell 2013 Dec 15
Online Access:View this record in EBSCOhost
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      jtl: Scientific World Journal
      issn: 1537744X
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      dt: 2013 Dec 15
      vid: 2013
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2013/387194
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        atl: A multipopulation PSO based memetic algorithm for permutation flow shop scheduling.
      aug:
        au:
          Liu, Ruochen
          Ma, Chenlin
          Ma, Wenping
          Li, Yangyang
        affil: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China.
      sug:
        subj:
          Algorithms
          Models, Statistical
      ab: The permutation flow shop scheduling problem (PFSSP) is part of production scheduling, which belongs to the hardest combinatorial optimization problem. In this paper, a multipopulation particle swarm optimization (PSO) based memetic algorithm (MPSOMA) is proposed in this paper. In the proposed algorithm, the whole particle swarm population is divided into three subpopulations in which each particle evolves itself by the standard PSO and then updates each subpopulation by using different local search schemes such as variable neighborhood search (VNS) and individual improvement scheme (IIS). Then, the best particle of each subpopulation is selected to construct a probabilistic model by using estimation of distribution algorithm (EDA) and three particles are sampled from the probabilistic model to update the worst individual in each subpopulation. The best particle in the entire particle swarm is used to update the global optimal solution. The proposed MPSOMA is compared with two recently proposed algorithms, namely, PSO based memetic algorithm (PSOMA) and hybrid particle swarm optimization with estimation of distribution algorithm (PSOEDA), on 29 well-known PFFSPs taken from OR-library, and the experimental results show that it is an effective approach for the PFFSP.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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