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International Conference on Swarm Intelligence

ICSI 2021: Advances in Swarm Intelligence pp 479–491Cite as

Brain Storm Optimization Algorithm Based on Formal Concept Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12689))

Abstract

The brain storm optimization (BSO) algorithm is an excellent swarm intelligence paradigm, inspired from the behaviors of the human process of brainstorming. The design of BSO is characterized by the clustering mechanism. However, this mechanism is inefficient to deal with complex large-scale optimization problems. In this paper, we propose a high-dimensional BSO algorithm based on formal concept analysis (FCA), called HBSO, for dealing with large-scale optimization problems. In HBSO, two new procedures are developed, i.e., relationship analysis of individuals and adaptively determine the number of clusters. Relationship analysis is used to judge the similarity of individuals in the population. The FCA is used to determine the size of k in the original clustering algorithm, in order to alleviate the evolution stagnation of clusters. Experiments are conducted on a set of the CEC2017 benchmark functions and the results verify the effectiveness and efficiency of HBSO on the benchmark problems.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (61773103), the Intelligent Manufacturing Standardization and Test Verification Project “Time Sensitive Network (TSN) and Object Linking and Embedding Unified Architecture for Industrial Control OPC UA Fusion Key Technology Standard Research and Test Verification” project, Ministry of Industry and Information Technology of the People’s Republic of China, and National key research and development program of China, No. 2018YFB1700103.

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Authors and Affiliations

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

    Fengrong Chang, Lianbo Ma & Aoshuang Dong

  2. School of Physics, Liaoning University, Shenyang, China

    Yan Song

Authors
  1. Fengrong Chang
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  2. Lianbo Ma
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  3. Yan Song
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  4. Aoshuang Dong
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Corresponding author

Correspondence to Lianbo Ma .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Chang, F., Ma, L., Song, Y., Dong, A. (2021). Brain Storm Optimization Algorithm Based on Formal Concept Analysis. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_43

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  • DOI: https://doi.org/10.1007/978-3-030-78743-1_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78742-4

  • Online ISBN: 978-3-030-78743-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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