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Link to original content: https://doi.org/10.1007/978-3-319-99247-1_3
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The New Adaptive ETLBO Algorithms with K-Armed Bandit Model

  • Conference paper
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Knowledge Science, Engineering and Management (KSEM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11062))

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Abstract

TLBO is a novel efficient swarm intelligent algorithm. In this paper, we first analyze TLBO and ETLBO algorithms in detail. Aiming at the disadvantage of ETLBO that it has to adjust the number of elite according to the different problems, we propose an improved adaptive ETLBO algorithm AETLBO-KAB that is based on K-armed bandit model. Experiments are carried out on six popular continuous non-linear test functions, and the results show that AETLBO-KAB algorithm is effective and brings dramatic improvement compared with TLBO and ETLBO. Furthermore, a new perturbation strategy—discussion group strategy is proposed. And the experimental results indicate that the efficiency of AETLBO-KAB with discussion group algorithm exceeds AETLBO-KAB algorithm.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (61170314,61373052), the Project of Jilin Provincial Science and Technology Development(20170414004GH).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Xitong Wang, Yonggang Zhang & Jiaxu Cui

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun, 130012, China

    Xitong Wang, Yonggang Zhang & Jiaxu Cui

Authors
  1. Xitong Wang
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  2. Yonggang Zhang
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  3. Jiaxu Cui
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Corresponding author

Correspondence to Yonggang Zhang .

Editor information

Editors and Affiliations

  1. University of Bristol, Bristol, United Kingdom

    Weiru Liu

  2. Università di Trento, Povo, Italy

    Fausto Giunchiglia

  3. Jilin University, Changchun, China

    Bo Yang

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Wang, X., Zhang, Y., Cui, J. (2018). The New Adaptive ETLBO Algorithms with K-Armed Bandit Model. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11062. Springer, Cham. https://doi.org/10.1007/978-3-319-99247-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-99247-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99246-4

  • Online ISBN: 978-3-319-99247-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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