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Modified Self-adaptive Brain Storm Optimization Algorithm for Multimodal Optimization

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

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Abstract

Multimodal Optimization is one of the most challenging tasks for optimization, since many real-world problems may have multiple acceptable solutions. Different from single objective optimization problem, multimodal optimization needs to both find multiple optima/peaks at the same time, and maintain these found optima until the end of a run. A novel swarm intelligent method, Modified Self-adaptive Brain Storm Optimization (MSBSO) algorithm is proposed to solve multimodal optimization problems in this paper. In order to find potential multiple optima, a modified disruption strategy is used for BSO algorithms to maintain the identified optima until the end of the search. Besides, the self-adaptive cluster number control is applied to improve Max-fitness Clustering Method with no need for a predefined subpopulation size M. Eight multimodal benchmark functions are used to validate the performance and effectiveness. Compared with the other swarm intelligent algorithms reported in the literature, the new algorithm can outperform others on most of the test functions.

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Acknowledgments

This work is supported by the Guangdong-Hong Kong Joint Innovation Platform of Big Data and Computational Intelligence under Grant 2018B050502006.

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Authors and Affiliations

  1. Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Ze-yu Dai & Qing Li

  2. Jiangnan University, Wuxi, China

    Wei Fang

  3. South China University of Technology, Guangzhou, China

    Wei-neng Chen

Authors
  1. Ze-yu Dai
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  2. Wei Fang
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  3. Qing Li
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  4. Wei-neng Chen
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Correspondence to Ze-yu Dai .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Dai, Zy., Fang, W., Li, Q., Chen, Wn. (2020). Modified Self-adaptive Brain Storm Optimization Algorithm for Multimodal Optimization. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_30

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

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