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Reference sharing: a new collaboration model for cooperative coevolution

Author

Listed:
  • Min Shi

    (Norwegian University of Science and Technology)

  • Shang Gao

    (Örebro University)

Abstract

Cooperative coevolutionary algorithms have been a popular and effective learning approach to solve optimization problems through problem decomposition. However, their performance is highly sensitive to the degree of problem separability. Different collaboration mechanisms usually have to be chosen for particular problems. In the paper, we aim to design a collaboration model that can be successfully applied to a wide range of problems. We present a novel collaboration mechanism that offers this type of potential, along with a new sorting strategy for individuals that are assigned multiple fitness values. Furthermore, we demonstrate and analyze our algorithm through comparison studies with other popular cooperative coevolutionary models on a suite of standard function optimization problems.

Suggested Citation

  • Min Shi & Shang Gao, 2017. "Reference sharing: a new collaboration model for cooperative coevolution," Journal of Heuristics, Springer, vol. 23(1), pages 1-30, February.
    • Handle: RePEc:spr:joheur:v:23:y:2017:i:1:d:10.1007_s10732-016-9322-9
    DOI: 10.1007/s10732-016-9322-9
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    Cited by:

    1. Chengshuai Li & Biao Zhang & Yuyan Han & Yuting Wang & Junqing Li & Kaizhou Gao, 2022. "Energy-Efficient Hybrid Flowshop Scheduling with Consistent Sublots Using an Improved Cooperative Coevolutionary Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-27, December.

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