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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2017: Simulated Evolution and Learning pp 994–1002Cite as

Visualizing the Search Dynamics in a High-Dimensional Space for a Particle Swarm Optimizer

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

Abstract

Visualization of an evolutionary algorithm may lead to better understanding of how it works. In this paper, three dimension reduction techniques (i.e. PCA, Sammon mapping, and recently developed t-SNE) are compared and analyzed empirically for visualizing the search dynamics of a particle swarm optimizer. Specifically, the search path of the global best position of a particle swarm optimizer over iterations is depicted in a low-dimensional space. Visualization results simulated on a variety of continuous functions show that (1) t-SNE could display the evolution of search path but its performance deteriorates as the dimension increases, and t-SNE tends to enlarge the search path generated during the later search stage; (2) the local search behavior (e.g. convergence to the optimum) can be identified by PCA with more stable performance than its two competitors, though for which it may be difficult to clearly depict the global search path; (3) Sammon mapping suffers easily from the overlapping problem. Furthermore, some important practical issues on how to appropriately interpret visualization results in the low-dimensional space are also highlighted.

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Notes

  1. 1.

    Note that all the source code will be publicly available on Github.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Qiqi Duan, Chang Shao, Xiaodong Li & Yuhui Shi

  2. School of Science (Computer Science and Software Engineering), RMIT University, Melbourne, Australia

    Xiaodong Li

Authors
  1. Qiqi Duan
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  2. Chang Shao
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  3. Xiaodong Li
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  4. Yuhui Shi
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Corresponding author

Correspondence to Chang Shao .

Editor information

Editors and Affiliations

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

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

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

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Duan, Q., Shao, C., Li, X., Shi, Y. (2017). Visualizing the Search Dynamics in a High-Dimensional Space for a Particle Swarm Optimizer. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_82

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_82

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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