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Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems

Author

Listed:
  • Xiaoqiu Shi
  • Wei Long
  • Yanyan Li
  • Dingshan Deng

Abstract

A genetic algorithm (GA) cannot always avoid premature convergence, and multi-population is usually used to overcome this limitation by dividing the population into several sub-populations (sub-population number) with the same number of individuals (sub-population size). In previous research, the questions of how a network structure composed of sub-populations affects the propagation rate of advantageous genes among sub-populations and how it affects the performance of GA have always been ignored. Therefore, we first propose a multi-population GA with an ER network (MPGA-ER). Then, by using the flexible job shop scheduling problem (FJSP) as an example and considering the total individual number (TIN), we study how the sub-population number and size and the propagation rate of advantageous genes affect the performance of MPGA-ER, wherein the performance is evaluated by the average optimal value and success rate based on TIN. The simulation results indicate the following regarding the performance of MPGA-ER: (i) performance shows considerable improvement compared with that of traditional GA; (ii) for an increase in the sub-population number for a certain TIN, the performance first increases slowly, and then decreases rapidly; (iii) for an increase in the sub-population size for a certain TIN, the performance of MPGA-ER first increases rapidly and then tends to remain stable; and (iv) with an increase in the propagation rate of advantageous genes, the performance first increases rapidly and then decreases slowly. Finally, we use a parameter-optimized MPGA-ER to solve for more FJSP instances and demonstrate its effectiveness by comparing it with that of other algorithms proposed in other studies.

Suggested Citation

  • Xiaoqiu Shi & Wei Long & Yanyan Li & Dingshan Deng, 2020. "Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-23, May.
  • Handle: RePEc:plo:pone00:0233759
    DOI: 10.1371/journal.pone.0233759
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    References listed on IDEAS

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    1. Erfan Babaee Tirkolaee & Ali Asghar Rahmani Hosseinabadi & Mehdi Soltani & Arun Kumar Sangaiah & Jin Wang, 2018. "A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services," Sustainability, MDPI, vol. 10(5), pages 1-21, April.
    2. Changxi Ma & Wei Hao & Fuquan Pan & Wang Xiang, 2018. "Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-22, June.
    3. Hayato Goto & Hideki Takayasu & Misako Takayasu, 2017. "Estimating risk propagation between interacting firms on inter-firm complex network," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-12, October.
    4. Preis, Tobias & Bardoscia, Marco & Caccioli, Fabio & Perotti, Juan Ignacio & Vivaldo, Gianna & Caldarelli, Guido, 2016. "Distress propagation in complex networks: the case of non-linear DebtRank," LSE Research Online Documents on Economics 68598, London School of Economics and Political Science, LSE Library.
    5. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    6. Zengliang Han & Dongqing Wang & Feng Liu & Zhiyong Zhao, 2017. "Multi-AGV path planning with double-path constraints by using an improved genetic algorithm," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-16, July.
    7. Marco Bardoscia & Fabio Caccioli & Juan Ignacio Perotti & Gianna Vivaldo & Guido Caldarelli, 2016. "Distress Propagation in Complex Networks: The Case of Non-Linear DebtRank," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-12, October.
    8. Marco Bardoscia & Fabio Caccioli & Juan Ignacio Perotti & Gianna Vivaldo & Guido Caldarelli, 2015. "Distress propagation in complex networks: the case of non-linear DebtRank," Papers 1512.04460, arXiv.org, revised Sep 2016.
    9. Deng, Zheng-Hong & Huang, Yi-Jie & Gu, Zhi-Yang & Liu, Dan & Gao, Li, 2018. "Multigames with voluntary participation on interdependent networks and the evolution of cooperation," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 151-157.
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