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Two-Stage Learning Brain Storm Optimizer

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Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 12463))

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Abstract

Brain storm optimizer (BSO), a new swarm intelligence paradigm inspired from the human brainstorming process, have received a surge of attentions. However, the original BSO easily suffers from the premature convergence due to its ineffective solution generation operation. In this paper, a two-stage learning strategy is proposed to accelerate the efficiency of the solution generation operation in BSO, thereby enhancing the convergence speed as well as the diversity of population. At the first stage, a learning automaton strategy is conducted to select an appropriate learning exemplar to guide the updating of each solution (i.e., idea). This strategy learns from the feedback information from the environment to enhance the exploration and exploitation. At the second stage, a comprehensive learning strategy is used to generate a set of directional learning exemplars, using utilize useful search experiences during the search. The experimental results on a set of CEC2017 benchmarks validate the effectiveness of the proposed strategy. Then, the resultant algorithm called ACLBSO is applied to resolving the quantitative association rule mining problem. Simulation results show that ACLBSO is a satisfactory optimizer to deal with the complex association rule mining problems.

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Acknowledgment

This work was supported in part by National Natural Science Foundation of China under Grant No. 61773103 and Huawei HIRP project under No. HO2019085002.

Author information

Authors and Affiliations

  1. College of Software, Northeastern University, Shenyang, China

    Yan Xu, Jingwei Wang & Lianbo Ma

  2. 2012 Laboratories, Huawei Technologies Co., Ltd., Shenzhen, China

    Junfeng Zhao & Xiaolong Shen

Authors
  1. Yan Xu

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  2. Jingwei Wang

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  3. Lianbo Ma

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  4. Junfeng Zhao

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  5. Xiaolong Shen

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Corresponding authors

Correspondence toJingwei Wang orLianbo Ma.

Editor information

Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Xu, Y., Wang, J., Ma, L., Zhao, J., Shen, X. (2020). Two-Stage Learning Brain Storm Optimizer. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_3

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