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Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems

Author

Listed:
  • Yadong Yu
  • Haiping Ma
  • Mei Yu
  • Sengang Ye
  • Xiaolei Chen

Abstract

Multipopulation is an effective optimization strategy which is often used in evolutionary algorithms (EAs) to improve optimization performance. However, it is of remarkable difficulty to determine the number of subpopulations during the evolution process for a given problem, which may significantly affect optimization ability of EAs. This paper proposes a simple multipopulation management strategy to dynamically adjust the subpopulation number in different evolution phases throughout the evolution. The proposed method makes use of individual distances in the same subpopulation as well as the population distances between multiple subpopulations to determine the subpopulation number, which is substantial in maintaining population diversity and enhancing the exploration ability. Furthermore, the proposed multipopulation management strategy is embedded into popular EAs to solve real-world complex automated warehouse scheduling problems. Experimental results show that the proposed multipopulation EAs can easily be implemented and outperform other regular single-population algorithms to a large extent.

Suggested Citation

  • Yadong Yu & Haiping Ma & Mei Yu & Sengang Ye & Xiaolei Chen, 2018. "Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems," Complexity, Hindawi, vol. 2018, pages 1-14, April.
    • Handle: RePEc:hin:complx:4730957
    DOI: 10.1155/2018/4730957
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    References listed on IDEAS

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    1. Xin Li & Jingang Lai & Ruoli Tang, 2017. "A Hybrid Constraints Handling Strategy for Multiconstrained Multiobjective Optimization Problem of Microgrid Economical/Environmental Dispatch," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    2. Kunjie Yu & Xin Wang & Zhenlei Wang, 2016. "An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 831-843, August.
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    Cited by:

    1. Wu Lin & Qiuzhen Lin & Zexuan Zhu & Jianqiang Li & Jianyong Chen & Zhong Ming, 2019. "Evolutionary Search with Multiple Utopian Reference Points in Decomposition-Based Multiobjective Optimization," Complexity, Hindawi, vol. 2019, pages 1-22, April.

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