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Multi-population interactive coevolutionary algorithm for flexible job shop scheduling problems

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Abstract

In this paper, it proposes a multi-population interactive coevolutionary algorithm for the flexible job shop scheduling problems. In the proposed algorithm, both the ant colony optimization and genetic algorithm with different configurations were applied to evolve each population independently. By the interaction, competition and sharing mechanism among populations, the computing resource is utilized more efficiently, and the quality of populations is improved effectively. The performance of our proposed approach was evaluated by a lot of benchmark instances taken from literature. The experimental results have shown that the proposed algorithm is a feasible and effective approach for the flexible job shop scheduling problem.

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Authors and Affiliations

  1. College of Information System and Management, National University of Defense Technology, Changsha, China

    Li-Ning Xing, Ying-Wu Chen & Ke-Wei Yang

Authors
  1. Li-Ning Xing
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  2. Ying-Wu Chen
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  3. Ke-Wei Yang
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Corresponding author

Correspondence to Li-Ning Xing.

Additional information

This work was supported by the National Natural Science Foundation of China (No. 70272002), Graduate Student Public Sending Abroad Project for Constructing High-level University of China Scholarship Council, Innovative Foundation for Excellent Graduate Student of National University of Defense Technology.

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Xing, LN., Chen, YW. & Yang, KW. Multi-population interactive coevolutionary algorithm for flexible job shop scheduling problems. Comput Optim Appl 48, 139–155 (2011). https://doi.org/10.1007/s10589-009-9244-7

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