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International Conference on Parallel Problem Solving from Nature

PPSN 2020: Parallel Problem Solving from Nature – PPSN XVI pp 650–663Cite as

A SHADE-Based Algorithm for Large Scale Global Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12269))

Abstract

During the last decade, large-scale global optimization has been a very active research area not only because of its many challenges but also because of its high applicability. It is indeed crucial to develop more effective search strategies to explore large search spaces considering limited computational resources. In this paper, we propose a new hybrid algorithm called Global and Local search using Success-History Based Parameter Adaptation for Differential Evolution (GL-SHADE) which was specifically designed for large-scale global optimization. Our proposed approach uses two populations that evolve differently allowing them to complement each other during the search process. One is in charge of exploring the search space while the other is in charge of exploiting it. Our proposed method is evaluated using the CEC’2013 large-scale global optimization (LSGO) test suite with 1000 decision variables. Our experimental results show that the new proposal outperforms one of the best hybrid algorithms available in the state of the art (SHADEILS) in the majority of the test problems adopted while being competitive with respect to several other state-of-the-art algorithms when using the LSGO competition criteria adopted at CEC’2019.

The first author acknowledges support from CONACyT and CINVESTAV-IPN to pursue graduate studies in Computer Science. The second author gratefully acknowledges support from CONACyT grant no. 2016-01-1920 (Investigación en Fronteras de la Ciencia 2016) and from a SEP-Cinvestav grant (application no. 4).

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Notes

  1. 1.

    Without loss of generality, we will assume minimization.

  2. 2.

    Our source code can be obtained from: https://github.com/delmoral313/gl-shade.

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Author information

Authors and Affiliations

  1. Computer Science Department, CINVESTAV-IPN (Evolutionary Computation Group), Av. IPN No. 2508, Col. San Pedro Zacatenco, México D.F., 07300, Mexico

    Oscar Pacheco-Del-Moral & Carlos A. Coello Coello

Authors
  1. Oscar Pacheco-Del-Moral
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  2. Carlos A. Coello Coello
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Corresponding author

Correspondence to Oscar Pacheco-Del-Moral .

Editor information

Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Thomas Bäck

  2. Leiden University, Leiden, The Netherlands

    Mike Preuss

  3. Leiden University, Leiden, The Netherlands

    André Deutz

  4. Sorbonne University, Paris, France

    Hao Wang

  5. Sorbonne University, Paris, France

    Carola Doerr

  6. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  7. University of Münster, Münster, Germany

    Heike Trautmann

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Pacheco-Del-Moral, O., Coello Coello, C.A. (2020). A SHADE-Based Algorithm for Large Scale Global Optimization. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12269. Springer, Cham. https://doi.org/10.1007/978-3-030-58112-1_45

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  • DOI: https://doi.org/10.1007/978-3-030-58112-1_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58111-4

  • Online ISBN: 978-3-030-58112-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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