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Gbest-Guided Covariance Matrix Adaptation Evolution Strategy for Large Scale Global Optimization

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Abstract

The optimization of a large number of decision variables, so called large scale global optimization (LSGO) remains challenging for existing heuristics. Inspired by the concept of global best (gbest) guided strategy, this paper proposes a gbest-guided covariance matrix adaptation evolution strategy (GCMA-ES) where the gbest information is utilized in the search equation to guide the exploitation process. The GCMA-ES can take advantages from both the CMA-ES and the gbest-guided strategy. Its performance is demonstrated on the CEC 2010 LSGO benchmarks.

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Acknowledgements

The work is financially supported by the National Science Foundation of China (No. 71571187, 71201170, 61403404) and the Distinguished Natural Science Foundation of Hunan Province (2017JJ1001).

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Authors and Affiliations

  1. College of Information Systems and Management, National University of Defense Technology, Changsha, 410073, People’s Republic of China

    Zhang Fuxing, Zhang Tao & Wang Rui

  2. State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, 410073, People’s Republic of China

    Zhang Tao

Authors
  1. Zhang Fuxing
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  2. Zhang Tao
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  3. Wang Rui
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Corresponding author

Correspondence to Wang Rui .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

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Fuxing, Z., Tao, Z., Rui, W. (2017). Gbest-Guided Covariance Matrix Adaptation Evolution Strategy for Large Scale Global Optimization. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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