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Density-Based Population Initialization Strategy for Continuous Optimization

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Bio-Inspired Computing: Theories and Applications (BIC-TA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1363))

Abstract

The population initialization is the first and crucial step in many swarm intelligence and evolutionary algorithms. In this paper, we propose a new density-based population initialization strategy, which is concerned about both uniformity and randomness of the initial population. In experiments, first, the empty space statistic is adopted to indicate its favorable uniformity. Then, the proposed strategy is used to generate an initial population for CMA-ES, and compared with typical initialization strategies over the CEC-2013 multimodal optimization benchmark. The experimental results demonstrate that the density-based initialization strategy could generate more uniform distribution than the random strategy, and such a strategy is beneficial to evolutionary multimodal optimization.

This work is partly supported by the National Natural Science Foundation of China (No. 61573327).

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China

    Peilan Xu, Jiafei Xu & Yingying Qiao

  2. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518000, Guangdong, China

    Wenjian Luo & Jiajia Zhang

Authors
  1. Peilan Xu
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  2. Wenjian Luo
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  3. Jiafei Xu
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  4. Yingying Qiao
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  5. Jiajia Zhang
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Corresponding author

Correspondence to Wenjian Luo .

Editor information

Editors and Affiliations

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

    Linqiang Pan

  2. China University of Petroleum, Qingdao, China

    Shangchen Pang

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. China University of Petroleum, Qingdao, China

    Faming Gong

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Xu, P., Luo, W., Xu, J., Qiao, Y., Zhang, J. (2021). Density-Based Population Initialization Strategy for Continuous Optimization. In: Pan, L., Pang, S., Song, T., Gong, F. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2020. Communications in Computer and Information Science, vol 1363. Springer, Singapore. https://doi.org/10.1007/978-981-16-1354-8_5

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  • DOI: https://doi.org/10.1007/978-981-16-1354-8_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1353-1

  • Online ISBN: 978-981-16-1354-8

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