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A Dynamic Global Differential Grouping for Large-Scale Black-Box Optimization

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Advances in Swarm Intelligence (ICSI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10941))

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Abstract

Cooperative Co-evolution (CC) framework is an important method to tackle Large Scale Black-Box Optimization (LSBO) problem. One of the main step in CC is grouping for the decision variables, which affects the optimization performance. An ideal grouping result is that the relationship of decision variables in intra-group is stronger as possible and those in inter-groups is weaker as possible. Global Differential Grouping (GDG) is an efficient grouping method based on the idea of partial derivatives of multivariate functions, and it can automatically resolve the problem by maintaining the global information among variables. However, once the grouping result by GDG is determined, it will no longer be updated and will not be automatically adjusted with the evolution of the algorithm, which may affect the optimization performance of the algorithm. Therefore, based on GDG, a Dynamic Global Differential Grouping (DGDG) strategy is proposed for grouping the decision variables in this paper, which can update the grouping results with the evolution processing. DGDG works with Particle Swarm Optimization (PSO) algorithm in this paper, which is termed as CC-DGDG-PSO. The experimental results based on the LSBO benchmark functions from CEC’2010 show that DGDG algorithm can improve the performance of GDG.

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Acknowledgement

This work was partially supported by the National Natural Science foundation of China (Grant Nos. 61673194, 61105128), Key Research and Development Program of Jiangsu Province, China (Grant No. BE2017630), the Postdoctoral Science Foundation of China (Grant No. 2014M560390), Six Talent Peaks Project of Jiangsu Province (Grant No. DZXX-025). National Key R&D Program of China, Project (Grant No. 2017YFC1601800).

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Authors and Affiliations

  1. School of IoT Engineering, Jiangnan University, Wuxi, China

    Shuai Wu & Wei Fang

  2. School of Science, Jiangnan University, Wuxi, China

    Zhitao Zou

Authors
  1. Shuai Wu
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  2. Zhitao Zou
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  3. Wei Fang
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Corresponding author

Correspondence to Wei Fang .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

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

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Wu, S., Zou, Z., Fang, W. (2018). A Dynamic Global Differential Grouping for Large-Scale Black-Box Optimization. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_56

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  • DOI: https://doi.org/10.1007/978-3-319-93815-8_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93814-1

  • Online ISBN: 978-3-319-93815-8

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