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Low-Dimensional Space Modeling-Based Differential Evolution: A Scalability Perspective on bbob-largescale suite

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Advances in Computational Intelligence (IWANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14134))

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Abstract

Scalability is a challenge for Large Scale Optimization Problems (LSGO). Improving the scalability of efficient Differential Evolution algorithms (DE) has been a research focus due to their successful application to high-dimensional problems. Recently, a DE-based algorithm called LSMDE (Low-dimensional Space Modeling-based Differential Evolution) has shown promising results in solving LSGO problems on the CEC’2013 large-scale global optimization suite. LSMDE uses dimensionality reduction to generate an alternative search space and Gaussian mixture models to deal with the information loss caused by uncertainty from space transformation. This paper aims to extend the initial research through the scalability analysis of the LSMDE’s performance compared with its main competitors, SHADE-ILS and GL-SHADE, on bbob-largescale suite functions. The results show that although all competing algorithms perform worse as dimensionality increases, LSMDE outperforms the competition and is robust to dimensionality expansion in search spaces with diverse characteristics, achieving a target hit rate between \(40\%\) and \(80\%\).

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Acknowledgment

This research was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and by a research grant from Science Foundation Ireland (SFI) under grant no. SFI/16/RC/3918 (CONFIRM) and Marie Sklodowska-Curie grant agreement no. 847.577 co-funded by the European Regional Development Fund.

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Authors and Affiliations

  1. Universidade Federal de Santa Catarina (UFSC), Florianópolis, SC, Brazil

    Thiago Henrique Lemos Fonseca & Silvia Modesto Nassar

  2. Universidade Federal do Maranhão (UFMA), São Luís, MA, Brazil

    Alexandre César Muniz de Oliveira

  3. University College Cork, Cork, CO, Ireland

    Alexandre César Muniz de Oliveira

  4. Polytechnique Montréal, Montreal, QC, Canada

    Bruno Agard

Authors
  1. Thiago Henrique Lemos Fonseca
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  2. Silvia Modesto Nassar
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  3. Alexandre César Muniz de Oliveira
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  4. Bruno Agard
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Corresponding author

Correspondence to Thiago Henrique Lemos Fonseca .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Málaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Spain

    Andreu Catala

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Fonseca, T.H.L., Nassar, S.M., de Oliveira, A.C.M., Agard, B. (2023). Low-Dimensional Space Modeling-Based Differential Evolution: A Scalability Perspective on bbob-largescale suite. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14134. Springer, Cham. https://doi.org/10.1007/978-3-031-43085-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-43085-5_2

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  • Online ISBN: 978-3-031-43085-5

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